Data-Engineer-Associate Sample Questions Answers

Option B is the best solution to meet the requirements with the least operational overhead because S3 Object Lambda is a feature that allows you to add your own code to process data retrieved from S3 before returning it to an application. S3 Object Lambda works with S3 GET requests and can modify both the object metadata and the object data. By using S3 Object Lambda, you can implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data. This way, you can avoid creating and maintaining multiple copies of the dataset with different levels of redaction.

Option A is not a good solution because it involves creating and managing multiple copies of the dataset with different levels of redaction for each application. This option adds complexity and storage cost to the data protection process and requires additional resources and configuration. Moreover, S3 bucket policies cannot enforce fine-grained data access control at the row and column level, so they are not sufficient to redact PII.

Option C is not a good solution because it involves using AWS Glue to transform the data for each application. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. However, in this scenario, using AWS Glue to redact PII is not the best option because it requires creating and maintaining multiple copies of the dataset with different levels of redaction for each application. This option also adds extra time and cost to the data protection process and requires additional resources and configuration.

Option D is not a good solution because it involves creating and configuring an API Gateway endpoint that has custom authorizers. API Gateway is a service that allows you to create, publish, maintain, monitor, and secure APIs at any scale. API Gateway can also integrate with other AWS services, such as Lambda, to provide custom logic for processing requests. However, in this scenario, using API Gateway to redact PII is not the best option because it requires writing and maintaining custom code and configuration for the API endpoint, the custom authorizers, and the REST API call. This option also adds complexity and latency to the data protection process and requires additional resources and configuration.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Introducing Amazon S3 Object Lambda – Use Your Code to Process Data as It Is Being Retrieved from S3

Using Bucket Policies and User Policies - Amazon Simple Storage Service

AWS Glue Documentation

What is Amazon API Gateway? - Amazon API Gateway

The telecommunications company needs a low-latency solution to detect sudden drops in network usage from real-time data collected throughout the day.

Option B: Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (Amazon Kinesis Data Analytics) application to detect drops in network usage.Using Amazon Kinesis with Managed Service for Apache Flink (formerly Kinesis Data Analytics) is ideal for real-time stream processing with minimal latency. Flink can analyze the incoming data stream in real-time and detect anomalies, such as sudden drops in usage, which makes it the best fit for this scenario.

Other options (A, C, and D) either introduce unnecessary delays (e.g., querying databases) or do not provide the same real-time, low-latency processing that is critical for this use case.

AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue Studio is a graphical interface that allows you to easily author, run, and monitor AWS Glue ETL jobs. AWS Glue Data Quality is a feature that enables you to validate, cleanse, and enrich your data using predefined or custom rules. AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows.

Using the Detect PII transform in AWS Glue Studio, you can automatically identify and label the PII in your dataset, such as names, addresses, phone numbers, email addresses, etc. You can then create a rule in AWS Glue Data Quality to obfuscate the PII, such as masking, hashing, or replacing the values with dummy data. You can also use other rules to validate and cleanse your data, such as checking for null values, duplicates, outliers, etc. You can then use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake. You can use AWS Glue DataBrew to visually explore and transform the data, AWS Glue crawlers to discover and catalog the data, and AWS Glue jobs to load the data into the S3 data lake.

This solution will meet the requirement with the least operational effort, as it leverages the serverless and managed capabilities of AWS Glue, AWS Glue Studio, AWS Glue Data Quality, and AWS Step Functions. You do not need to write any code to identify or obfuscate the PII, as you can use the built-in transforms and rules in AWS Glue Studio and AWS Glue Data Quality. You also do not need to provision or manage any servers or clusters, as AWS Glue and AWS Step Functions scale automatically based on the demand.

The other options are not as efficient as using the Detect PII transform in AWS Glue Studio, creating a rule in AWS Glue Data Quality, and using an AWS Step Functions state machine. Using an Amazon Kinesis Data Firehose delivery stream to process the dataset, creating an AWS Lambda transform function to identify the PII, using an AWS SDK to obfuscate the PII, and setting the S3 data lake as the target for the delivery stream will require more operational effort, as you will need to write and maintain code to identify and obfuscate the PII, as well as manage the Lambda function and its resources. Using the Detect PII transform in AWS Glue Studio to identify the PII, obfuscating the PII, and using an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake will not be as effective as creating a rule in AWS Glue Data Quality to obfuscate the PII, as you will need to manually obfuscate the PII after identifying it, which can be error-prone and time-consuming. Ingesting the dataset into Amazon DynamoDB, creating an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data, and using the same Lambda function to ingest the data into the S3 data lake will require more operational effort, as you will need to write and maintain code to identify and obfuscate the PII, as well as manage the Lambda function and its resources. You will also incur additional costs and complexity by using DynamoDB as an intermediate data store, which may not be necessary for your use case. References:

AWS Glue

AWS Glue Studio

AWS Glue Data Quality

[AWS Step Functions]

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 6: Data Integration and Transformation, Section 6.1: AWS Glue

Step 1: Understanding the Data Use Case

The company has data stored in an Amazon S3 bucket and needs to provide teams access for analysis, ensuring that PII data is not included in the analysis. The solution should be simple to implement and maintain, ensuring minimal operational overhead.

Step 2: Why Option D is Correct

Option D (AWS Glue DataBrew) allows you to visually prepare and transform data without needing to write code. By using a DataBrew job, the company can:

Automatically detect and separate PII data from non-PII data.

Store PII data in a second S3 bucket for security, while keeping the original S3 bucket clean for analysis.

This approach keeps operational overhead low by utilizing DataBrew's pre-built transformations and the easy-to-use interface for non-technical users. It also ensures compliance by separating sensitive PII data from the main dataset.

Step 3: Why Other Options Are Not Ideal

Option A (Amazon Macie) is a powerful tool for detecting sensitive data, but Macie doesn't inherently remove or mask PII. You would still need additional steps to clean the data after Macie identifies PII.

Option B (S3 Object Lambda with Amazon Comprehend) introduces more complexity by requiring custom logic at the point of data access. Amazon Comprehend can detect PII, but using S3 Object Lambda to filter data would involve more overhead.

Option C (Kinesis Data Firehose and Comprehend) is more suitable for real-time streaming data use cases rather than batch analysis. Setting up and managing a streaming solution like Kinesis adds unnecessary complexity.

Conclusion:

Using AWS Glue DataBrew provides a low-overhead, no-code solution to detect and separate PII data, ensuring the analysis teams only have access to non-sensitive data. This approach is simple, compliant, and easy to manage compared to other options.

Amazon DynamoDB is optimized for Query operations, which retrieve items efficiently using the partition key and optionally the sort key. Queries consume significantly fewer RCUs than Scan operations because only matching partitions are read.

By performing a Query using the partition key in the key condition expression, the AWS Glue job reads only the required items directly from DynamoDB. This minimizes the number of read capacity units consumed and avoids unnecessary reads.

Using a Scan operation, even when followed by filtering in a DynamicFrame, consumes RCUs for the entire table or index and is highly inefficient. Using only the sort key in a query is invalid without the partition key and would not meet DynamoDB query requirements. The Glue DynamoDB connector filter option still performs a scan under the hood and does not minimize RCUs.

The queried data can be directly loaded into a DynamicFrame, satisfying the existing processing logic requirement without additional transformations.

Therefore, Option D provides the most efficient and cost-effective solution.

Athena workgroups are a way to isolate query execution and query history among users, teams, and applications that share the same AWS account. By creating a workgroup for each use case, the company can control the access and actions on the workgroup resource using resource-level IAM permissions or identity-based IAM policies. The company can also use tags to organize and identify the workgroups, and use them as conditions in the IAM policies to grant or deny permissions to the workgroup. This solution meets the requirements of separating query processes and access to query history among users, teams, and applications that are in the same AWS account. References:

Athena Workgroups

IAM policies for accessing workgroups

Workgroup example policies

AWS Secrets Manager is a service that allows you to securely store and manage secrets, such as database credentials, API keys, passwords, etc. You can use Secrets Manager to encrypt, rotate, and audit your secrets, as well as to control access to them using fine-grained policies. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API).

Storing the credentials in AWS Secrets Manager and granting the AWS Glue job 1AM role access to the stored credentials will meet the requirements, as it will remediate the security vulnerability in the AWS Glue job and securely store the credentials. By using AWS Secrets Manager, you can avoid hard coding the credentials in the job script, which is a bad practice that exposes the credentials to unauthorized access or leakage. Instead, you can store the credentials as a secret in Secrets Manager and reference the secret name or ARN in the job script. You can also use Secrets Manager to encrypt the credentials using AWS Key Management Service (AWS KMS), rotate the credentials automatically or on demand, and monitor the access to the credentials using AWS CloudTrail. By granting the AWS Glue job 1AM role access to the stored credentials, you can use the principle of least privilege to ensure that only the AWS Glue job can retrieve the credentials from Secrets Manager. You can also use resource-based or tag-based policies to further restrict the access to the credentials.

The other options are not as secure as storing the credentials in AWS Secrets Manager and granting the AWS Glue job 1AM role access to the stored credentials. Storing the credentials in the AWS Glue job parameters will not remediate the security vulnerability, as the job parameters are still visible in the AWS Glue console and API. Storing the credentials in a configuration file that is in an Amazon S3 bucket and accessing the credentials from the configuration file by using the AWS Glue job will not be as secure as using Secrets Manager, as the configuration file may not be encrypted or rotated, and the access to the file may not be audited or controlled. References:

AWS Secrets Manager

AWS Glue

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.1: AWS Glue

To address the requirement of masking PII data and ensuring secure access throughout the data pipeline, the combination of AWS Glue DataBrew and IAM provides a low-maintenance solution.

A. AWS Glue DataBrew for Masking:

AWS Glue DataBrew provides a visual tool to perform data transformations, including masking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.

Amazon Redshift supports Role-Based Access Control (RBAC) to manage access to database objects. RBAC allows administrators to create roles for job functions and assign privileges at the schema, table, or column level based on data sensitivity and user roles.

“RBAC in Amazon Redshift helps manage permissions more efficiently at scale by assigning users to roles that reflect their job function. It simplifies user management and secures access based on job role and data sensitivity.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

RBAC is preferred over RLS or CLS alone because it offers a more comprehensive and scalable solution across multiple users and permissions.

AWS Glue workflows are a feature of AWS Glue that enable you to create and visualize complex ETL pipelines using AWS Glue components, such as crawlers, jobs, triggers, and development endpoints. AWS Glue workflows provide automated orchestration and require minimal manual effort, as they handle dependency resolution, error handling, state management, and resource allocation for your ETL workflows. You can use AWS Glue workflows to ingest data from your operational databases into your Amazon S3 based data lake, and then use AWS Glue and Amazon EMR to process the data in the data lake. This solution will meet the requirements with the least operational overhead, as it leverages the serverless and fully managed nature of AWS Glue, and the scalability and flexibility of Amazon EMR12.

The other options are not optimal for the following reasons:

B. AWS Step Functions tasks. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use AWS Step Functions tasks to invoke AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Step Functions state machines to define the logic and flow of your workflows. However, this option would require more manual effort than AWS Glue workflows, as you would need to write JSON code to define your state machines, handle errors and retries, and monitor the execution history and status of your workflows3.

C. AWS Lambda functions. AWS Lambda is a service that lets you run code without provisioning or managing servers. You can use AWS Lambda functions to trigger AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Lambda event sources and destinations to orchestrate the flow of your workflows. However, this option would also require more manual effort than AWS Glue workflows, as you would need to write code to implement your business logic, handle errors and retries, and monitor the invocation and execution of your Lambda functions. Moreover, AWS Lambda functions have limitations on the execution time, memory, and concurrency, which may affect the performance and scalability of your ETL workflows.

D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows. Amazon MWAA is a managed service that makes it easy to run open source Apache Airflow on AWS. Apache Airflow is a popular tool for creating and managing complex ETL pipelines using directed acyclic graphs (DAGs). You can use Amazon MWAA workflows to orchestrate AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use the Airflow web interface to visualize and monitor your workflows. However, this option would have more operational overhead than AWS Glue workflows, as you would need to set up and configure your Amazon MWAA environment, write Python code to define your DAGs, and manage the dependencies and versions of your Airflow plugins and operators.

1: AWS Glue Workflows

2: AWS Glue and Amazon EMR

3: AWS Step Functions

AWS Lambda

Amazon Managed Workflows for Apache Airflow

To allow cross-account access to a Kinesis Data Stream, you must add a resource-based policy to the Kinesis stream in Account A, explicitly granting the Lambda execution role in Account B the required permissions.

SCPs (A & C) set permissions boundaries, but do not grant access.

Option D incorrectly refers to the Lambda function – but the Kinesis resource must allow access.

“You must add a resource-based policy to the Kinesis Data Stream in Account A to allow a Lambda function in Account B to consume from the stream.”

The requirement is to manage the size of an Amazon DynamoDB table by automatically deleting data older than 1 month without disrupting ongoing read or write operations. The simplest and most maintenance-free solution is to use DynamoDB Time-to-Live (TTL).

Option A: Use the DynamoDB TTL feature to automatically expire data based on timestamps.DynamoDB TTL allows you to specify an attribute (e.g., a timestamp) that defines when items in the table should expire. After the expiration time, DynamoDB automatically deletes the items, freeing up storage space and keeping the table size under control without manual intervention or disruptions to ongoing operations.

Other options involve higher maintenance and manual scheduling or scanning operations, which increase complexity unnecessarily compared to the native TTL feature.

The best solution to meet the requirements of giving data scientists the ability to query all data sources by using syntax similar to SQL with the least operational overhead is to use AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use Amazon Athena to query the data, use SQL for structured data sources, and use PartiQL for data that is stored in JSON format.

AWS Glue is a serverless data integration service that makes it easy to prepare, clean, enrich, and move data between data stores1. AWS Glue crawlers are processes that connect to a data store, progress through a prioritized list of classifiers to determine the schema for your data, and then create metadata tables in the Data Catalog2. The Data Catalog is a persistent metadata store that contains table definitions, job definitions, and other control information to help you manage your AWS Glue components3. You can use AWS Glue to crawl the data sources, such as Amazon S3, Amazon RDS for Microsoft SQL Server, and Amazon DynamoDB, and store the metadata in the Data Catalog.

Amazon Athena is a serverless, interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL or Python4. Amazon Athena also supports PartiQL, a SQL-compatible query language that lets you query, insert, update, and delete data from semi-structured and nested data, such as JSON. You can use Amazon Athena to query the data from the Data Catalog using SQL for structured data sources, such as .csv files and relational databases, and PartiQL for data that is stored in JSON format. You can also use Athena to query data from other data sources, such as Amazon Redshift, using federated queries.

Using AWS Glue and Amazon Athena to query all data sources by using syntax similar to SQL is the least operational overhead solution, as you do not need to provision, manage, or scale any infrastructure, and you pay only for the resources you use. AWS Glue charges you based on the compute time and the data processed by your crawlers and ETL jobs1. Amazon Athena charges you based on the amount of data scanned by your queries. You can also reduce the cost and improve the performance of your queries by using compression, partitioning, and columnar formats for your data in Amazon S3.

Option B is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, and use Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena. Redshift Spectrum is a feature of Amazon Redshift, a fully managed data warehouse service, that allows you to query and join data across your data warehouse and your data lake using standard SQL. While Redshift Spectrum is powerful and useful for many data warehousing scenarios, it is not necessary or cost-effective for querying all data sources by using syntax similar to SQL. Redshift Spectrum charges you based on the amount of data scanned by your queries, which is similar to Amazon Athena, but it also requires you to have an Amazon Redshift cluster, which charges you based on the node type, the number of nodes, and the duration of the cluster5. These costs can add up quickly, especially if you have large volumes of data and complex queries. Moreover, using Redshift Spectrum would introduce additional latency and complexity, as you would have to provision and manage the cluster, and create an external schema and database for the data in the Data Catalog, instead of querying it directly from Amazon Athena.

Option C is not the best solution, as using AWS Glue to crawl the data sources, store metadata in the AWS Glue Data Catalog, use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format, store the transformed data in an S3 bucket, and use Amazon Athena to query the original and transformed data from the S3 bucket, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Glue jobs are ETL scripts that you can write in Python or Scala to transform your data and load it to your target data store. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes6. While using AWS Glue jobs and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to write, run, and monitor the ETL jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using AWS Glue jobs and Parquet would introduce additional latency, as you would have to wait for the ETL jobs to finish before querying the transformed data.

Option D is not the best solution, as using AWS Lake Formation to create a data lake, use Lake Formation jobs to transform the data from all data sources to Apache Parquet format, store the transformed data in an S3 bucket, and use Amazon Athena or Redshift Spectrum to query the data, would incur more costs and complexity than using Amazon Athena with PartiQL. AWS Lake Formation is a service that helps you centrally govern, secure, and globally share data for analytics and machine learning7. Lake Formation jobs are ETL jobs that you can create and run using the Lake Formation console or API. While using Lake Formation and Parquet can improve the performance and reduce the cost of your queries, they would also increase the complexity and the operational overhead of the data pipeline, as you would have to create, run, and monitor the Lake Formation jobs, and store the transformed data in a separate location in Amazon S3. Moreover, using Lake Formation and Parquet would introduce additional latency, as you would have to wait for the Lake Formation jobs to finish before querying the transformed data. Furthermore, using Redshift Spectrum to query the data would also incur the same costs and complexity as mentioned in option B. References:

What is Amazon Athena?

Data Catalog and crawlers in AWS Glue

AWS Glue Data Catalog

Columnar Storage Formats

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

AWS Glue Schema Registry

What is AWS Glue?

Amazon Redshift Serverless

Amazon Redshift provisioned clusters

[Querying external data using Amazon Redshift Spectrum]

[Using stored procedures in Amazon Redshift]

[What is AWS Lambda?]

[PartiQL for Amazon Athena]

[Federated queries in Amazon Athena]

[Amazon Athena pricing]

[Top 10 performance tuning tips for Amazon Athena]

[AWS Glue ETL jobs]

[AWS Lake Formation jobs]

Amazon Redshift data sharing now supports bi-directional and selective write access through datashare namespaces.

This allows centralized management of permissions from headquarters while giving Regional clusters controlled write access to specific datasets — adhering to least privilege and minimizing admin overhead.

“Use Redshift datashares with write permissions and namespaces to enable controlled cross-Region data collaboration while maintaining centralized governance.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This configuration ensures near real-time inventory updates, centralized access management, and compliance with AWS security best practices.

Amazon Athena is a serverless, interactive query service that enables you to analyze data in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build, secure, and manage data lakes on AWS. You can use AWS Lake Formation to create data filters that define the level of access for different IAM roles based on the columns, rows, or tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to create data filters, the company can meet the requirements of ensuring that user groups can access only the PII that they require with the least effort. The solution is to use Amazon Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake Formation and create data filters to establish levels of access for the company’s IAM roles. For example, a data filter can allow a user group to access only the columns that contain the PII that they need, such as name and email address, and deny access to the columns that contain the PII that they do not need, such as phone number and social security number. Finally, assign each user to the IAM role that matches the user’s PII access requirements. This way, the user groups can access the data in the data lake securely and efficiently. The other options are either not feasible or not optimal. Using Amazon QuickSight to access the data (option B) would require the company to pay for the QuickSight service and to configure the column-level security features for each user. Building a custom query builder UI that will run Athena queries in the background to access the data (option C) would require the company to develop and maintain the UI and to integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular access (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. References:

Amazon Athena

AWS Lake Formation

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena

The data engineer needs to detect custom categories of PII within the data lake using AWS Glue DataBrew. While DataBrew provides standard data quality rules, the solution must support custom PII categories.

Option B: Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.This option is the most efficient because DataBrew allows the creation of custom data quality rules that can be applied to detect specific data patterns, including custom PII categories. This approach minimizes operational overhead while ensuring that the specific privacy requirements are met.

Options A, C, and D either involve manual intervention or developing custom scripts, both of which increase operational effort compared to using DataBrew's built-in capabilities.

Amazon RDS controls network-level access through security groups, which act as virtual firewalls. To allow an EC2 instance to connect to an RDS database using JDBC, the RDS security group must explicitly allow inbound traffic from the EC2 instance or its associated security group on the appropriate database port.

IAM roles do not control network connectivity to RDS databases. The ec2_authorized_hosts parameter does not exist for RDS engines. The Amazon RDS Data API is not supported for standard JDBC connections and is only available for Aurora Serverless v1 and v2.

Updating the RDS security group is the correct and simplest approach. By adding an inbound rule that allows traffic from the EC2 instance’s security group, the application can securely connect to the database without additional configuration or restarts.

Therefore, Option C is the correct solution.

A “No space left on device” error typically results from data skew or large shuffle stages. The best actions are:

B. Monitor using Spark UI and Glue metrics to find skewed partitions or executor issues.

D. Use --write-shuffle-files-to-s3 to offload intermediate data to S3 instead of local disk, and apply salting to reduce skew.

“You can reduce the impact of data skew and large shuffle operations by monitoring with Spark UI and enabling the --write-shuffle-files-to-s3 option. Salting can help rebalance the skewed keys.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Scaling out workers (A, C) is more costly and less efficient if the root cause (skew) is not fixed.

Problem Analysis:

The company must log all user activities and connection activities in Amazon Redshift for security compliance.

Key Considerations:

Redshift supports audit logging, which can be configured to write logs to an S3 bucket.

S3 provides durable, scalable, and cost-effective storage for logs.

Solution Analysis:

Option A: S3 for Logging

Standard approach for storing Redshift logs.

Easy to set up and manage with minimal cost.

Option B: Amazon EFS

EFS is unnecessary for this use case and less cost-efficient than S3.

Option C: Aurora MySQL

Using a database to store logs increases complexity and cost.

Option D: EBS Volume

EBS is not a scalable option for log storage compared to S3.

Final Recommendation:

Enable Redshift audit logging and specify an S3 bucket as the destination.

Amazon Redshift Audit Logging

Storing Logs in Amazon S3

A sort-merge join generates large shuffle files, leading to “No space left on device” errors when both datasets are large. Using a broadcast join sends a smaller dataset to all executors, avoiding shuffle and disk I/O overhead.

“Broadcast joins reduce shuffle I/O by distributing the smaller dataset to all worker nodes, mitigating disk space and shuffle errors.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This is the most cost-effective and direct fix for large shuffle-stage failures.

The RootDiskUsed metric for the MSK cluster indicates that the storage on the broker is reaching its capacity. The best solution is to expand the storage of the MSK broker and enable automatic storage expansion to prevent future alarms.

Expand MSK Broker Storage:

AWS Managed Streaming for Apache Kafka (MSK) allows you to expand the broker storage to accommodate growing data volumes. Additionally, auto-expansion of storage can be configured to ensure that storage grows automatically as the data increases.

 This solution meets the requirements of optimizing the performance of table SQL queries without increasing the size of the cluster. By using the ALL distribution style for rarely updated small tables, you can ensure that the entire table is copied to every node in the cluster, which eliminates the need for data redistribution during joins. This can improve query performance significantly, especially for frequently joined dimension tables. However, using the ALL distribution style also increases the storage space and the load time, so it is only suitable for small tables that are not updated frequently or extensively. By specifying primary and foreign keys for all tables, you can help the query optimizer to generate better query plans and avoid unnecessary scans or joins. You can also use the AUTO distribution style to let Amazon Redshift choose the optimal distribution style based on the table size and the query patterns. References:

Choose the best distribution style

Distribution styles

Working with data distribution styles

To prevent unintentional file deletions and meet the requirement with minimal operational overhead, enabling S3 Versioning is the best solution.

S3 Versioning:

S3 Versioning allows multiple versions of an object to be stored in the same S3 bucket. When a file is deleted or overwritten, S3 preserves the previous versions, which means you can recover from accidental deletions or modifications.

Enabling versioning requires minimal overhead, as it is a bucket-level setting and does not require additional backup processes or data replication.

Users can recover specific versions of files that were unintentionally deleted, meeting the needs of the data engineer to avoid accidental data loss.

AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows. You can use Step Functions to create state machines that define the sequence and logic of the tasks in your workflow. Step Functions supports various types of tasks, such as Lambda functions, AWS Glue jobs, Amazon EMR clusters, Amazon ECS tasks, etc. You can use Step Functions to monitor and troubleshoot your workflows, as well as to handle errors and retries.

Using an AWS Step Functions workflow that includes a state machine to run the Lambda function and then the AWS Glue job will meet the requirements with the least management overhead, as it leverages the serverless and managed capabilities of Step Functions. You do not need to write any code to orchestrate the tasks in your workflow, as you can use the Step Functions console or the AWS Serverless Application Model (AWS SAM) to define and deploy your state machine. You also do not need to provision or manage any servers or clusters, as Step Functions scales automatically based on the demand.

The other options are not as efficient as using an AWS Step Functions workflow. Using an Apache Airflow workflow that is deployed on an Amazon EC2 instance or on Amazon Elastic Kubernetes Service (Amazon EKS) will require more management overhead, as you will need to provision, configure, and maintain the EC2 instance or the EKS cluster, as well as the Airflow components. You will also need to write and maintain the Airflow DAGs to orchestrate the tasks in your workflow. Using an AWS Glue workflow to run the Lambda function and then the AWS Glue job will not work, as AWS Glue workflows only support AWS Glue jobs and crawlers as tasks, not Lambda functions. References:

AWS Step Functions

AWS Glue

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.3: AWS Step Functions

AWS Glue Data Quality transforms allow you to define built-in rules like IsComplete for null validation and ReferentialIntegrity for relationship validation—all with minimal code and operational overhead.

“Use AWS Glue Data Quality rules such as IsComplete and ReferentialIntegrity within ETL jobs to automatically validate incoming data.”

The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here’s why:

Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.

Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.

Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.

Alternatives Considered:

Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard-IA directly would be more cost-effective since the pattern of access is well-known (frequent for 30 days, infrequent thereafter).

Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.

Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.

Amazon S3 Lifecycle Policies

S3 Storage Classes

Cost Management and Data Optimization Using Lifecycle Policies

AWS Data Engineering Documentation

The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. References:

Using the Amazon Redshift Data API

Using Amazon EventBridge with Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost. Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data. The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D) would not work, as the data on the instance store volume would be lost if the instance is terminated. References:

Amazon EC2 Instance Store

Amazon EBS Volumes

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2

Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze streaming data in real time. You can use Kinesis Data Streams to capture sensor data from various sources, such as IoT devices, web applications, or mobile apps. You can create data streams that can scale up to handle any amount of data from thousands of producers. You can also use the Kinesis Client Library (KCL) or the Kinesis Data Streams API to write applications that process and analyze the data in the streams1.

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. You can use DynamoDB to store the sensor data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can use the DynamoDB API or the AWS SDKs to perform queries on the data, such as using key-value lookups, scans, or queries2.

The solution that meets the requirements with the least operational overhead is to use Amazon Kinesis Data Streams to capture the sensor data and store the data in Amazon DynamoDB for querying. This solution has the following advantages:

It does not require you to provision, manage, or scale any servers, clusters, or queues, as Kinesis Data Streams and DynamoDB are fully managed services that handle all the infrastructure for you. This reduces the operational complexity and cost of running your solution.

It allows you to ingest sensor data in near real time, as Kinesis Data Streams can capture data records as they are produced and deliver them to your applications within seconds. You can also use Kinesis Data Firehose to load the data from the streams to DynamoDB automatically and continuously3.

It allows you to store the data in nested JSON format, as DynamoDB supports document data types, such as lists and maps. You can also use DynamoDB Streams to capture changes in the data and trigger actions, such as sending notifications or updating other databases.

It allows you to query the data with a latency of less than 10 milliseconds, as DynamoDB offers single-digit millisecond performance for any scale of data. You can also use DynamoDB Accelerator (DAX) to improve the read performance by caching frequently accessed data.

Option A is incorrect because it suggests using a self-hosted Apache Kafka cluster to capture the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own Kafka cluster, either on EC2 instances or on-premises servers. This increases the operational complexity and cost of running your solution.

It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency.

Option B is incorrect because it suggests using AWS Lambda to process the sensor data and store the data in Amazon S3 for querying. This solution has the following disadvantages:

It does not allow you to ingest sensor data in near real time, as Lambda is a serverless compute service that runs code in response to events. You need to use another service, such as API Gateway or Kinesis Data Streams, to trigger Lambda functions with sensor data, which may add extra latency and complexity to your solution.

It does not allow you to query the data with a latency of less than 10 milliseconds, as Amazon S3 is an object storage service that is not optimized for low-latency queries. You need to use another service, such as Amazon Athena or Amazon Redshift Spectrum, to query the data in S3, which may incur additional costs and latency.

Option D is incorrect because it suggests using Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data and use AWS Glue to store the data in Amazon RDS for querying. This solution has the following disadvantages:

It does not allow you to ingest sensor data in near real time, as Amazon SQS is a message queue service that delivers messages in a best-effort manner. You need to use another service, such as Lambda or EC2, to poll the messages from the queue and process them, which may add extra latency and complexity to your solution.

It does not allow you to store the data in nested JSON format, as Amazon RDS is a relational database service that supports structured data types, such as tables and columns. You need to use another service, such as AWS Glue, to transform the data from JSON to relational format, which may add extra cost and overhead to your solution.

1: Amazon Kinesis Data Streams - Features

2: Amazon DynamoDB - Features

3: Loading Streaming Data into Amazon DynamoDB - Amazon Kinesis Data Firehose

[4]: Capturing Table Activity with DynamoDB Streams - Amazon DynamoDB

[5]: Amazon DynamoDB Accelerator (DAX) - Features

[6]: Amazon S3 - Features

[7]: AWS Lambda - Features

[8]: Amazon Simple Queue Service - Features

[9]: Amazon Relational Database Service - Features

[10]: Working with JSON in Amazon RDS - Amazon Relational Database Service

[11]: AWS Glue - Features

Using an Amazon EventBridge rule to run an AWS Glue job or invoke an AWS Glue workflow job every 15 minutes are two possible solutions that will meet the requirements. AWS Glue is a serverless ETL service that can process and load data from various sources to various targets, including Amazon Redshift. AWS Glue can handle different data formats, such as CSV, JSON, and Parquet, and also support schema evolution, meaning it can adapt to changes in the data schema over time. AWS Glue can also leverage Apache Spark to perform distributed processing and transformation of large datasets. AWS Glue integrates with Amazon EventBridge, which is a serverless event bus service that can trigger actions based on rules and schedules. By using an Amazon EventBridge rule, you can invoke an AWS Glue job or workflow every 15 minutes, and configure the job or workflow to run an AWS Glue crawler and then load the data into the Amazon Redshift tables. This way, you can build a cost-effective and scalable ETL pipeline that can handle data from 10 source systems and function correctly despite changes to the data schema.

The other options are not solutions that will meet the requirements. Option C, configuring an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket, and creating a second Lambda function to run the AWS Glue job, is not a feasible solution, as it would require a lot of Lambda invocations and coordination. AWS Lambda has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the ETL pipeline. Option D, configuring an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket, is not a necessary solution, as you can use an Amazon EventBridge rule to invoke the AWS Glue workflow directly, without the need for a Lambda function. Option E, configuring an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket, and configuring the AWS Glue job to put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream, is not a cost-effective solution, as it would incur additional costs for Lambda invocations and data delivery. Moreover, using Amazon Kinesis Data Firehose to load data into Amazon Redshift is not suitable for frequent and small batches of data, as it can cause performance issues and data fragmentation. References:

AWS Glue

Amazon EventBridge

Using AWS Glue to run ETL jobs against non-native JDBC data sources

[AWS Lambda quotas]

[Amazon Kinesis Data Firehose quotas]

Amazon Aurora zero-ETL integration with Amazon Redshift Serverless is designed specifically to provide near real-time analytics on transactional data with minimal operational overhead. This integration continuously replicates changes from Aurora MySQL databases into Redshift Serverless without requiring data pipelines, replication tasks, or infrastructure management.

Because the requirement emphasizes near real-time latency and minimal management, zero-ETL integration is the optimal choice. It eliminates the need to configure and monitor AWS DMS tasks, manage Glue jobs, or build and scale Lambda-based ingestion logic. Schema changes, data synchronization, and replication are handled automatically by AWS.

AWS DMS Serverless, while managed, still requires task configuration and monitoring and typically introduces more latency than zero-ETL integrations. AWS Glue jobs and Lambda functions require custom code, scheduling, and error handling, increasing both complexity and maintenance effort.

Therefore, setting up Aurora zero-ETL integration with Amazon Redshift Serverless best satisfies the requirements and aligns with AWS best practices for modern analytics architectures.

AWS Glue workflows are designed to orchestrate the ETL pipeline, and you can create data quality checks to ensure the uploaded datasets are complete before running reports. If there is an issue with the data, AWS Glue workflows can trigger an Amazon EventBridge event that sends a message to an SNS topic.

AWS Glue Workflows:

AWS Glue workflows allow users to automate and monitor complex ETL processes. You can include data quality actions to check for null values, data types, and other consistency checks.

In the event of incomplete data, an EventBridge event can be generated to notify via SNS.

Option C is the correct answer because the Map state is designed to process a collection of data in parallel by applying the same transformation to each element. The Map state can invoke a nested workflow for each element, which can be another state machine or a Lambda function. The Map state will wait until all the parallel executions are completed before moving to the next state.

Option A is incorrect because the Parallel state is used to execute multiple branches of logic concurrently, not to process a collection of data. The Parallel state can have different branches with different logic and states, whereas the Map state has only one branch that is applied to each element of the collection.

Option B is incorrect because the Choice state is used to make decisions based on a comparison of a value to a set of rules. The Choice state does not process any data or invoke any nested workflows.

Option D is incorrect because the Wait state is used to delay the state machine from continuing for a specified time. The Wait state does not process any data or invoke any nested workflows.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.3: AWS Step Functions, Pages 131-132

Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.2: AWS Step Functions, Pages 9-10

AWS Documentation Overview, AWS Step Functions Developer Guide, Step Functions Concepts, State Types, Map State, Pages 1-3

To migrate data from an Amazon RDS for PostgreSQL DB instance in the eu-east-1 Region (Account_A) to an Amazon Redshift cluster in the eu-west-1 Region (Account_B), AWS DMS needs a replication instance located in the target region (in this case, eu-west-1) to facilitate the data transfer between regions.

Option A: Set up an AWS DMS replication instance in Account_B in eu-west-1.Placing the DMS replication instance in the target account and region (Account_B in eu-west-1) is the most efficient solution. The replication instance can connect to the source RDS PostgreSQL in eu-east-1 and migrate the data to the Redshift cluster in eu-west-1. This setup ensures data is replicated across AWS accounts and regions.

Options B, C, and D place the replication instance in either the wrong account or region, which increases complexity without adding any benefit.

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.

Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, and scale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.

Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.

The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:

It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.

It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.

It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.

Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.

It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.

It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.

Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.

It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.

It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.

Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:

It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.

It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.

1: AWS Lambda - Features

2: Amazon Elastic Container Service - Features

3: Amazon Elastic Kubernetes Service - Features

[4]: Building API Gateway REST API with Lambda integration - Amazon API Gateway

[5]: Improving latency with Provisioned Concurrency - AWS Lambda

[6]: Integrating Amazon ECS with Amazon API Gateway - Amazon Elastic Container Service

[7]: Integrating Amazon EKS with Amazon API Gateway - Amazon Elastic Kubernetes Service

[8]: Managing concurrency for a Lambda function - AWS Lambda

Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.

Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns. S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.

Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR. Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive. However, Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.

Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage. Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

What Is AWS Lake Formation? - AWS Lake Formation

Using AWS Lake Formation with Amazon Athena - AWS Lake Formation

Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation

Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation

Using Bucket Policies and User Policies - Amazon Simple Storage Service

Apache Ranger

Apache Pig

What Is Amazon Redshift? - Amazon Redshift

Problem Analysis:

The company uses Kinesis Data Streams for both inventory management and reordering.

The Kinesis Producer Library (KPL) publishes data, and the Kinesis Client Library (KCL) consumes data.

Duplicate records were observed in the inventory reordering system.

Key Considerations:

Kinesis streams are designed for durability but may produce duplicates under certain conditions.

Factors such as network timeouts, shard splits, or changes in record processors can cause duplication.

Solution Analysis:

Option A: Network-Related Timeouts

If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.

Option B: High IteratorAgeMilliseconds

High iterator age suggests delays in processing but does not directly cause duplication.

Option C: Changes in Shards or Processors

Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.

Option D: AggregationEnabled Set to True

AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.

Option E: High max_records Value

A high max_records value increases batch size but does not lead to duplication.

Final Recommendation:

Network-related timeouts and changes in shards or processors are the most likely causes of duplicate data in this scenario.

Amazon Kinesis Data Streams Best Practices

Kinesis Producer Library (KPL) Overview

Kinesis Client Library (KCL) Overview

In AWS Glue, table partitions are managed as metadata objects within the AWS Glue Data Catalog. To add a new partition to an existing table, the correct and supported approach is to use the AWS Glue Data Catalog API, such as the CreatePartition operation, or equivalent console or SDK actions.

Updating the table schema alone does not create partitions or inform Glue about new partition values. Editing metadata files directly in Amazon S3 is unsupported and can corrupt the Data Catalog. Manually modifying the S3 bucket structure without registering partitions in Glue will result in Athena and other query engines being unable to recognize the partitions.

By adding partitions through the Glue Data Catalog API, query engines such as Amazon Athena and Amazon Redshift Spectrum can perform partition pruning, which significantly improves query performance by scanning only relevant data.

This method aligns with AWS best practices, ensures metadata consistency, and avoids unnecessary operational risk. Therefore, Option C is the correct solution.

 Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and Amazon Redshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks. Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines. References:

Amazon AppFlow

Amazon AppFlow | SaaS Integrations List

Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions

Problem Analysis:

The company runs a daily AWS Glue ETL pipeline to clean and transform files received in an S3 bucket.

If a file is incomplete or empty, the previous day’s file should be retained.

Need a solution to validate files before overwriting the existing file.

Key Considerations:

Automate data validation with minimal human intervention.

Use built-in AWS Glue capabilities for ease of integration.

Ensure robust validation for missing or incomplete data.

Solution Analysis:

Option A: Lambda Function for Validation

Lambda can validate files, but it would require custom code.

Does not leverage AWS Glue’s built-in features, adding operational complexity.

Option B: AWS Glue Data Quality Rules

AWS Glue Data Quality allows defining Data Quality Definition Language (DQDL) rules.

Rules can validate if required fields are missing or if the file is empty.

Automatically integrates into the existing ETL pipeline.

If validation fails, retain the previous day’s file.

Option C: AWS Glue Studio with Filling Missing Values

Modifying ETL code to fill missing values with most common values risks introducing inaccuracies.

Does not handle empty files effectively.

Option D: Athena Query for Validation

Athena can drop rows with missing values, but this is a post-hoc solution.

Requires manual intervention to copy the corrected file to S3, increasing complexity.

Final Recommendation:

Use AWS Glue Data Quality to define validation rules in DQDL for identifying missing or incomplete data.

This solution integrates seamlessly with the ETL pipeline and minimizes manual effort.

Implementation Steps:

Enable AWS Glue Data Quality in the existing ETL pipeline.

Define DQDL Rules, such as:

Check if a file is empty.

Verify required fields are present and non-null.

Configure the pipeline to proceed with overwriting only if the file passes validation.

In case of failure, retain the previous day’s file.

AWS Glue Data Quality Overview

Defining DQDL Rules

AWS Glue Studio Documentation

 This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in Amazon Redshift, you can access the data from Kinesis Data Streams using SQL queries without having to load the data into the cluster. By creating a materialized view on top of the stream, you can store the results of the query in the cluster and make them available for analysis. By setting the materialized view to auto refresh, you can ensure that the view is updated with the latest data from the stream at regular intervals. This way, you can derive near real-time insights by using existing BI and analytics tools. References:

Amazon Redshift streaming ingestion

Creating an external schema for Amazon Kinesis Data Streams

Creating a materialized view for Amazon Kinesis Data Streams

 The best solutions to resolve the performance bottleneck and reduce Athena query planning time are to create an AWS Glue partition index and enable partition filtering, and to use Athena partition projection based on the S3 bucket prefix.

AWS Glue partition indexes are a feature that allows you to speed up query processing of highly partitioned tables cataloged in AWS Glue Data Catalog. Partition indexes are available for queries in Amazon EMR, Amazon Redshift Spectrum, and AWS Glue ETL jobs. Partition indexes are sublists of partition keys defined in the table. When you create a partition index, you specify a list of partition keys that already exist on a given table. AWS Glue then creates an index for the specified keys and stores it in the Data Catalog. When you run a query that filters on the partition keys, AWS Glue uses the partition index to quickly identify the relevant partitions without scanning the entire table metadata. This reduces the query planning time and improves the query performance1.

Athena partition projection is a feature that allows you to speed up query processing of highly partitioned tables and automate partition management. In partition projection, Athena calculates partition values and locations using the table properties that you configure directly on your table in AWS Glue. The table properties allow Athena to ‘project’, or determine, the necessary partition information instead of having to do a more time-consuming metadata lookup in the AWS Glue Data Catalog. Because in-memory operations are often faster than remote operations, partition projection can reduce the runtime of queries against highly partitioned tables. Partition projection also automates partition management because it removes the need to manually create partitions in Athena, AWS Glue, or your external Hive metastore2.

Option B is not the best solution, as bucketing the data based on a column that the data have in common in a WHERE clause of the user query would not reduce the query planning time. Bucketing is a technique that divides data into buckets based on a hash function applied to a column. Bucketing can improve the performance of join queries by reducing the amount of data that needs to be shuffled between nodes. However, bucketing does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario3.

Option D is not the best solution, as transforming the data that is in the S3 bucket to Apache Parquet format would not reduce the query planning time. Apache Parquet is a columnar storage format that can improve the performance of analytical queries by reducing the amount of data that needs to be scanned and providing efficient compression and encoding schemes. However, Parquet does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario4.

Option E is not the best solution, as using the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects would not reduce the query planning time. S3DistCP is a tool that can copy large amounts of data between Amazon S3 buckets or from HDFS to Amazon S3. S3DistCP can also aggregate smaller files into larger files to improve the performance of sequential access. However, S3DistCP does not affect the partition metadata retrieval, which is the main cause of the performance bottleneck in this scenario5. References:

Improve query performance using AWS Glue partition indexes

Partition projection with Amazon Athena

Bucketing vs Partitioning

Columnar Storage Formats

S3DistCp

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Amazon DynamoDB is a serverless, low-latency, NoSQL database ideal for semi-structured data.

Adding DynamoDB Accelerator (DAX) provides microsecond response times for read-heavy workloads.

“For applications requiring millisecond or sub-millisecond reads with serverless operation, use DynamoDB with DAX caching.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

 AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don’t have to manage any infrastructure.

AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.

AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.

Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage. Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:

AWS Glue Workflows

AWS Step Functions

AWS Glue Studio

Amazon MWAA

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

 The best combination of resources to meet the requirements of high reliability, cost-optimization, and performance for running Apache Spark jobs on Amazon EMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodes and task nodes.

Amazon S3 is a highly durable, scalable, and secure object storage service that can store any amount of data for a variety of use cases, including big data analytics1. Amazon S3 is a better choice than HDFS as a persistent data store for Amazon EMR, as it decouples the storage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3 also supports data encryption, versioning, lifecycle management, and cross-region replication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System (EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistent view, which enables Amazon EMR to provide read-after-write consistency for Amazon S3 objects that are accessed through EMRFS2.

Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver up to 40% better price performance over comparable current generation x86-based instances3. Graviton instances are ideal for running workloads that are CPU-bound, memory-bound, or network-bound, such as big data analytics, web servers, and open-source databases3. Graviton instances are compatible with Amazon EMR, and can be used for both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local file system. Task nodes are optional nodes that can be added to a cluster to increase the processing power and throughput. By using Graviton instances for both core nodes and task nodes, you can achieve higher performance and lower cost than using x86-based instances.

Using Spot Instances for all primary nodes is not a good option, as it can compromise the reliability and availability of the cluster. Spot Instances are spare EC2 instances that are available at up to 90% discount compared to On-Demand prices, but they can be interrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primary nodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue, and are essential for the cluster operation. If a primary node is interrupted by EC2, the cluster will fail or become unstable. Therefore, it is recommended to use On-Demand Instances or Reserved Instances for primary nodes, and use Spot Instances only for task nodes that can tolerate interruptions. References:

Amazon S3 - Cloud Object Storage

EMR File System (EMRFS)

AWS Graviton2 Processor-Powered Amazon EC2 Instances

[Plan and Configure EC2 Instances]

[Amazon EC2 Spot Instances]

[Best Practices for Amazon EMR]

Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.

Amazon Athena Federated Queries:

Athena’s federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena’s support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.

The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.

Publishing AWS Glue data quality scores to Amazon DataZone requires creating a DQDL ruleset, scheduling it to run regularly, and then linking the corresponding AWS Glue table as a data source in the DataZone project. The setup ensures that data quality scores from Glue are correctly published and accessible within Amazon DataZone:

“You can define DQDL rulesets for Glue tables and publish the data quality results to DataZone when the project is configured with an AWS Glue data source and the rulesets are scheduled.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Option C follows the expected flow without unnecessary complexity and aligns perfectly with the integration flow supported by AWS.

 AWS DataSync is an online data movement and discovery service that simplifies and accelerates data migrations to AWS as well as moving data to and from on-premises storage, edge locations, other cloud providers, and AWS Storage services1. AWS DataSync can copy data to and from various sources and targets, including Amazon S3, and handle files in multiple formats. AWS DataSync also supports incremental transfers, meaning it can detect and copy only the changes to the data, reducing the amount of data transferred and improving the performance. AWS DataSync can automate and schedule the transfer process using triggers, and monitor the progress and status of the transfers using CloudWatch metrics and events1.

AWS DataSync is the most operationally efficient way to transfer the data in this scenario, as it meets all the requirements and offers a serverless and scalable solution. AWS Glue, AWS Direct Connect, and Amazon S3 Transfer Acceleration are not the best options for this scenario, as they have some limitations or drawbacks compared to AWS DataSync. AWS Glue is a serverless ETL service that can extract, transform, and load data from various sources to various targets, including Amazon S32. However, AWS Glue is not designed for large-scale data transfers, as it has some quotas and limits on the number and size of files it can process3. AWS Glue also does not support incremental transfers, meaning it would have to copy the entire data set every time, which would be inefficient and costly.

AWS Direct Connect is a service that establishes a dedicated network connection between your on-premises data center and AWS, bypassing the public internet and improving the bandwidth and performance of the data transfer. However, AWS Direct Connect is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS DataSync, AWS Storage Gateway, or AWS CLI. AWS Direct Connect also has some hardware and location requirements, and charges you for the port hours and data transfer out of AWS.

Amazon S3 Transfer Acceleration is a feature that enables faster data transfers to Amazon S3 over long distances, using the AWS edge locations and optimized network paths. However, Amazon S3 Transfer Acceleration is not a data transfer service by itself, as it requires additional services or tools to copy the data, such as AWS CLI, AWS SDK, or third-party software. Amazon S3 Transfer Acceleration also charges you for the data transferred over the accelerated endpoints, and does not guarantee a performance improvement for every transfer, as it depends on various factors such as the network conditions, the distance, and the object size. References:

AWS DataSync

AWS Glue

AWS Glue quotas and limits

[AWS Direct Connect]

[Data transfer options for AWS Direct Connect]

[Amazon S3 Transfer Acceleration]

[Using Amazon S3 Transfer Acceleration]

For Amazon QuickSight to access data in an Amazon S3 bucket that is encrypted with an AWS KMS key in a different AWS account, two distinct permissions are required: access to the S3 bucket and access to the KMS key.

First, the QuickSight service role in the BI-Account must be granted permission to access the S3 bucket. This is accomplished by adding the S3 bucket as a resource that the QuickSight service role can access and configuring the bucket policy in the Hub-Account to trust that role. Without explicit S3 permissions, QuickSight cannot read the objects.

Second, because the S3 bucket uses SSE-KMS encryption, QuickSight must also be authorized to use the KMS key. This requires adding an IAM policy to the QuickSight service role that allows kms:Decrypt and related permissions on the KMS key. Without KMS permissions, S3 access will fail even if the bucket policy allows access.

AWS RAM cannot be used to share S3 buckets, encryption of network connections is irrelevant to KMS permissions, and adding the KMS key as a “resource” alone is not sufficient without IAM permissions.

Therefore, Options B and D are correct.

Amazon RDS is a fully managed service that provides relational databases in the cloud. Amazon RDS for MySQL is one of the supported database engines that you can use to run your applications. Amazon RDS provides various features and tools to monitor and optimize the performance of your DB instances, such as Performance Insights, Enhanced Monitoring, CloudWatch metrics and alarms, etc.

Using the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization and optimizing the problematic queries will help reduce the CPU utilization of the DB instance. Performance Insights is a feature that allows you to analyze the load on your DB instance and determine what is causing performance issues. Performance Insights collects, analyzes, and displays database performance data using an interactive dashboard. You can use Performance Insights to identify the top SQL statements, hosts, users, or processes that are consuming the most CPU resources. You can also drill down into the details of each query and see the execution plan, wait events, locks, etc. By using Performance Insights, you can pinpoint the root cause of the high CPU utilization and optimize the queries accordingly. For example, you can rewrite the queries to make them more efficient, add or remove indexes, use prepared statements, etc.

Implementing caching to reduce the database query load will also help reduce the CPU utilization of the DB instance. Caching is a technique that allows you to store frequently accessed data in a fast and scalable storage layer, such as Amazon ElastiCache. By using caching, you can reduce the number of requests that hit your database, which in turn reduces the CPU load on your DB instance. Caching also improves the performance and availability of your application, as it reduces the latency and increases the throughput of your data access. You can use caching for various scenarios, such as storing session data, user preferences, application configuration, etc. You can also use caching for read-heavy workloads, such as displaying product details, recommendations, reviews, etc.

The other options are not as effective as using Performance Insights and caching. Modifying the database schema to include additional tables and indexes may or may not improve the CPU utilization, depending on the nature of the workload and the queries. Adding more tables and indexes may increase the complexity and overhead of the database, which may negatively affect the performance. Rebooting the RDS DB instance once each week will not reduce the CPU utilization, as it will not address the underlying cause of the high CPU load. Rebooting may also cause downtime and disruption to your application. Upgrading to a larger instance size may reduce the CPU utilization, but it will also increase the cost and complexity of your solution. Upgrading may also not be necessary if you can optimize the queries and reduce the database load by using caching. References:

Amazon RDS

Performance Insights

Amazon ElastiCache

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 3: Data Storage and Management, Section 3.1: Amazon RDS

In Amazon Redshift, a compound sort key is designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists of Region ID, Department ID, and Role ID. Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.

Option B: "Select * from Employee where Region ID='North America' and Department ID=20;"This query will perform well because it uses both the Region ID and Department ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.

Option C: "Select * from Employee where Department ID=20 and Region ID='North America';"This query also benefits from the compound sort key because it includes both Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.

Options A, D, and E are less optimal because they do not utilize the sort key as effectively:

Option A only filters by the Region ID, which may still use the sort key but does not take full advantage of the compound nature.

Option D uses only Role ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.

Option E filters on Region ID and Role ID but skips the Department ID column, making it less efficient for the compound sort key.

The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the query will return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:

Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors)

When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)

 QuickSight does not have access to the S3 bucket and QuickSight does not have access to decrypt S3 data are two possible factors that could cause the permissions-related errors. Amazon QuickSight is a business intelligence service that allows you to create and share interactive dashboards based on various data sources, including Amazon Athena. Amazon Athena is a serverless query service that allows you to analyze data stored in Amazon S3 using standard SQL. To use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket, you need to grant QuickSight access to both Athena and S3, as well as any encryption keys that are used to encrypt the S3 data. If QuickSight does not have access to the S3 bucket or the encryption keys, it will not be able to read the data from Athena and display it on the dashboard, resulting in an error message that indicates insufficient permissions.

The other options are not factors that could cause the permissions-related errors. Option A, there is no connection between QuickSight and Athena, is not a factor, as QuickSight supports Athena as a native data source, and you can easily create a connection between them using the QuickSight console or the API. Option B, the Athena tables are not cataloged, is not a factor, as QuickSight can automatically discover the Athena tables that are cataloged in the AWS Glue Data Catalog, and you can also manually specify the Athena tables that are not cataloged. Option E, there is no IAM role assigned to QuickSight, is not a factor, as QuickSight requires an IAM role to access any AWS data sources, including Athena and S3, and you can create and assign an IAM role to QuickSight using the QuickSight console or the API. References:

Using Amazon Athena as a Data Source

Granting Amazon QuickSight Access to AWS Resources

Encrypting Data at Rest in Amazon S3

Athena data source is a solution that allows you to use Spark to access Athena by using the Athena JDBC driver and the Spark SQL interface. You can use the Athena data source to create Spark DataFrames from Athena tables, run SQL queries on the DataFrames, and write the results back to Athena. The Athena data source supports various data formats, such as CSV, JSON, ORC, and Parquet, and also supports partitioned and bucketed tables. The Athena data source is a cost-effective and scalable way to use Spark to access Athena, as it does not require any additional infrastructure or services, and you only pay for the data scanned by Athena.

The other options are not solutions that give the company the ability to use Spark to access Athena. Option A, Athena query settings, is a feature that allows you to configure various parameters for your Athena queries, such as the output location, the encryption settings, the query timeout, and the workgroup. Option B, Athena workgroup, is a feature that allows you to isolate and manage your Athena queries and resources, such as the query history, the query notifications, the query concurrency, and the query cost. Option D, Athena query editor, is a feature that allows you to write and run SQL queries on Athena using the web console or the API. None of these options enable you to use Spark instead of SQL to generate analytics on Athena. References:

Using Apache Spark in Amazon Athena

Athena JDBC Driver

Spark SQL

Athena query settings

[Athena workgroups]

[Athena query editor]

 The error message indicates that the AWS Glue job cannot access the Amazon S3 bucket through the VPC endpoint. This could be because the VPC’s route table does not have the necessary routes to direct the traffic to the endpoint. To fix this, the data engineer must verify that the route table has an entry for the Amazon S3 service prefix (com.amazonaws.region.s3) with the target as the VPC endpoint ID. This will allow the AWS Glue job to use the VPC endpoint to access the S3 bucket without going through the internet or a NAT gateway. For more information, see Gateway endpoints. References:

Troubleshoot the AWS Glue error “VPC S3 endpoint validation failed”

Amazon VPC endpoints for Amazon S3

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

To perform cross-Region snapshot copying of an encrypted Redshift cluster, AWS documentation and the exam study guide clearly outline two essential steps:

You must create a snapshot copy grant in the destination Region. This allows Amazon Redshift to encrypt the snapshots using the specified AWS KMS key.

You must enable snapshot copying in the source Region and specify the name of the snapshot copy grant that was created in the destination Region.

From the study guide:

“To enable cross-region copy of encrypted snapshots, you must create a snapshot copy grant in the destination Region and enable snapshot copying in the source Region by specifying the snapshot copy grant name.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Option E (Multi-AZ deployment) is not applicable to Amazon Redshift, which does not support Multi-AZ configurations like Amazon RDS.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables fast and cost-effective analysis of large volumes of data. Amazon Redshift uses columnar storage, compression, and zone maps to optimize the storage and performance of data. However, over time, as data is inserted, updated, or deleted, the physical storage of data can become fragmented, resulting in wasted disk space and degraded query performance. To address this issue, Amazon Redshift provides the VACUUM command, which reclaims disk space and resorts rows in either a specified table or all tables in the current schema1.

The VACUUM command has four options: FULL, DELETE ONLY, SORT ONLY, and REINDEX. The option that best meets the requirements of the question is VACUUM REINDEX, which re-sorts the rows in a table that has an interleaved sort key and rewrites the table to a new location on disk. An interleaved sort key is a type of sort key that gives equal weight to each column in the sort key, and stores the rows in a way that optimizes the performance of queries that filter by multiple columns in the sort key. However, as data is added or changed, the interleaved sort order can become skewed, resulting in suboptimal query performance. The VACUUM REINDEX option restores the optimal interleaved sort order and reclaims disk space by removing deleted rows. This option also analyzes the sort key column and updates the table statistics, which are used by the query optimizer to generate the most efficient query execution plan23.

The other options are not optimal for the following reasons:

A. VACUUM FULL Orders. This option reclaims disk space by removing deleted rows and resorts the entire table. However, this option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option is the most resource-intensive and time-consuming, as it rewrites the entire table to a new location on disk.

B. VACUUM DELETE ONLY Orders. This option reclaims disk space by removing deleted rows, but does not resort the table. This option is not suitable for tables that have any sort key, as it does not improve the query performance by restoring the sort order. Moreover, this option does not analyze the sort key column and update the table statistics.

D. VACUUM SORT ONLY Orders. This option resorts the entire table, but does not reclaim disk space by removing deleted rows. This option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option does not analyze the sort key column and update the table statistics.

1: Amazon Redshift VACUUM

2: Amazon Redshift Interleaved Sorting

3: Amazon Redshift ANALYZE

This solution will meet the requirements with the lowest latency because it uses Amazon Managed Service for Apache Flink to process the sensor data in real time and write it to Amazon Timestream, a fast, scalable, and serverless time series database. Amazon Timestream is optimized for storing and analyzing time series data, such as sensor data, and can handle trillions of events per day with millisecond latency. By using Amazon Timestream as a source, you can create an Amazon QuickSight dashboard that displays a real-time view of operational efficiency on a large screen in the manufacturing facility. Amazon QuickSight is a fully managed business intelligence service that can connect to various data sources, including Amazon Timestream, and provide interactive visualizations and insights123.

The other options are not optimal for the following reasons:

A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is similar to option C, but it uses Grafana instead of Amazon QuickSight to create the dashboard. Grafana is an open source visualization tool that can also connect to Amazon Timestream, but it requires additional steps to set up and configure, such as deploying a Grafana server on Amazon EC2, installing the Amazon Timestream plugin, and creating an IAM role for Grafana to access Timestream. These steps can increase the latency and complexity of the solution.

B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. This option is not suitable for displaying a real-time view of operational efficiency, as it introduces unnecessary delays and costs in the data pipeline. First, the sensor data is written to an S3 bucket by Amazon Kinesis Data Firehose, which can have a buffering interval of up to 900 seconds. Then, the S3 bucket sends a notification to a Lambda function, which can incur additional invocation and execution time. Finally, the Lambda function publishes the data to Amazon Aurora, a relational database that is not optimized for time series data and can have higher storage and performance costs than Amazon Timestream .

D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is also not suitable for displaying a real-time view of operational efficiency, as it uses AWS Glue bookmarks to read sensor data from the S3 bucket. AWS Glue bookmarks are a feature that helps AWS Glue jobs and crawlers keep track of the data that has already been processed, so that they can resume from where they left off. However, AWS Glue jobs and crawlers are not designed for real-time data processing, as they can have a minimum frequency of 5 minutes and a variable start-up time. Moreover, this option also uses Grafana instead of Amazon QuickSight to create the dashboard, which can increase the latency and complexity of the solution .

1: Amazon Managed Streaming for Apache Flink

2: Amazon Timestream

3: Amazon QuickSight

Analyze data in Amazon Timestream using Grafana

Amazon Kinesis Data Firehose

Amazon Aurora

AWS Glue Bookmarks

AWS Glue Job and Crawler Scheduling

Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS, such as PII. By configuring Macie for automated sensitive data discovery, the company can minimize manual intervention while ensuring PII is identified before data is shared.

Amazon Neptune supports graph applications using Gremlin and SPARQL as query languages. Neptune is a fully managed graph database service that supports both property graph and RDF graph models.

Option A: GremlinGremlin is a query language for property graph databases, which is supported by Amazon Neptune. It allows the traversal and manipulation of graph data in the property graph model.

Option D: SPARQLSPARQL is a query language for querying RDF graph data in Neptune. It is used to query, manipulate, and retrieve information stored in RDF format.

Other options:

SQL (Option B) and ANSI SQL (Option C) are traditional relational database query languages and are not used for graph databases.

Spark SQL (Option E) is related to Apache Spark for big data processing, not for querying graph databases.

Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.

AWS Glue Detect PII transform is designed to identify sensitive data using custom pattern matching, making it well suited for detecting PII in proprietary or non-standard data formats. The transform integrates directly into AWS Glue ETL jobs and requires minimal configuration beyond defining the detection patterns.

Amazon Macie primarily relies on managed data identifiers and machine learning models optimized for common data formats such as JSON, CSV, and text. While Macie supports custom identifiers, it is less efficient for deeply proprietary formats and introduces additional service configuration.

Using AWS Lambda with custom regular expressions or Amazon Athena with SQL-based pattern matching would require building, operating, and maintaining custom logic across multiple buckets, increasing operational overhead and complexity.

AWS Glue provides a serverless, scalable, and centralized approach for PII detection as part of an existing data processing pipeline, making it the most operationally efficient solution for this requirement.

Therefore, Option A is the best answer.

Amazon Athena Federated Query enables SQL queries across multiple data sources without moving data. Athena can directly query Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3 using data source connectors. This capability is ideal for one-time or ad hoc analytical queries, where setting up ingestion pipelines would introduce unnecessary cost and delay.

Because the requirement is for a one-time performance report, the solution should minimize operational overhead and data duplication. Athena Federated Query executes queries in place, eliminating ETL processes, data exports, and staging layers. This significantly reduces query preparation time and avoids storage costs associated with copying data into a centralized location.

The other options require extensive data movement and setup. DynamoDB Streams, AWS DMS, Glue ETL pipelines, and Amazon EMR clusters are appropriate for ongoing analytics pipelines, not for a single analytical report. These approaches increase operational complexity and execution time.

Athena Federated Query is purpose-built for this exact scenario and provides the fastest path to insight with minimal overhead.

The correct solution is C: AWS Secrets Manager.

Why? AWS Secrets Manager is a fully managed service that helps you protect access to your applications, services, and IT resources without the upfront cost and complexity of managing your own hardware security module (HSM) infrastructure.

It allows for automatic rotation of secrets without requiring changes to the application code, meeting the requirement of minimal operational overhead.

Applications can securely retrieve credentials using Secrets Manager APIs, and the service integrates with AWS Identity and Access Management (IAM) to control access to secrets.

“You can use Secrets Manager to store credentials and to configure automatic rotation.”