DVA-C02 Sample Questions Answers

This solution will meet the requirements by using AWS SAM CLI tool, which is a command line tool that lets developers locally build, test, debug, and deploy serverless applications defined by AWS SAM templates. The developer can use sam local generate-event command to generate sample events for different event sources such as API Gateway or S3. The developer can create automated test scripts that use sam local invoke command to invoke Lambda functions locally in an environment that closely simulates Lambda environment. The developer can check the response from Lambda functions and document how to run the test scripts for other developers on the team. The developer can also update CI/CD pipeline to run these test scripts before deploying with AWS CDK. Option A is not optimal because it will use cdk local invoke command, which does not exist in AWS CDK CLI tool. Option B is not optimal because it will use a unit testing framework that reproduces Lambda execution environment, which may not be accurate or consistent with Lambda environment. Option D is not optimal because it will create a Docker container from Node.js base image to invoke Lambda functions, which may introduce additional overhead and complexity for creating and running Docker containers.

References: [AWS Serverless Application Model (AWS SAM)], [AWS Cloud Development Kit (AWS CDK)]

The X-Ray daemon is a software that collects trace data from the X-Ray SDK and relays it to the X-Ray service. The X-Ray daemon can run on any platform that supports Go, including Linux, Windows, and macOS. The developer can install and run the X-Ray daemon on the on-premises servers to capture and relay the data to the X-Ray service with minimal configuration. The X-Ray SDK is used to instrument the application code, not to capture and relay data. The Lambda function solutions are more complex and require additional configuration.

References:

[AWS X-Ray concepts - AWS X-Ray]

[Setting up AWS X-Ray - AWS X-Ray]

Comprehensive and Detailed Step-by-Step Explanation:

Option D: Use Lambda with Container Images

AWS Lambda supports container images up to 10 GB in size, making it suitable for applications with large dependencies, such as a video codec library larger than 250 MB.

By creating separate container images for video compression and decompression, the application can efficiently isolate functionality while ensuring that each function includes the required dependencies.

The container images are stored in Amazon ECR and used to create the Lambda functions.

Why Other Options Are Incorrect:

Option A: A single Lambda function with all functionalities and dependencies in one zip file is not feasible due to the 250 MB deployment package size limit for zip files.

Option B: Including the library in two separate zip files still exceeds the size limit for Lambda zip deployment packages.

Option C: While using a Lambda layer can reduce redundancy, the combined size of the layer and the zip files would exceed the limit of 250 MB.

References:

Using Container Images with AWS Lambda

Amazon DynamoDB is a fully managed NoSQL database service that provides fast and consistent performance with seamless scalability. The developer can create a DynamoDB table and configure the table with a primary key that consists of the title as the partition key and the release year as the sort key. This will enable querying for a given title and release year efficiently. The developer can also create a global secondary index that uses the genre as the partition key and the title as the sort key. This will enable querying for a given genre efficiently. The developer can store additional properties about the cast and production crew as attributes in the DynamoDB table. These attributes can have different data types and structures, and they do not need to be consistent across items.

References:

[Amazon DynamoDB]

[Working with Queries - Amazon DynamoDB]

[Working with Global Secondary Indexes - Amazon DynamoDB]

The solution that will meet the requirements is to write data to Amazon ElastiCache. This way, the application can write session data to a fast, scalable, and reliable in-memory data store that can be served reliably across multiple requests. The other options either involve writing data to persistent storage, which is slower and more expensive than in-memory storage, or writing data to the root filesystem, which is not shared among multiple EC2 instances.

This solution will meet the requirements by using Amazon CloudWatch Logs to capture and analyze the logs from API Gateway. Amazon CloudWatch Logs is a service that monitors, stores, and accesses log files from AWS resources. The developer can turn on execution logging and access logging in Amazon CloudWatch Logs for the API stage, which enables logging information about API execution and client access to the API. The developer can create a CloudWatch Logs log group, which is a collection of log streams that share the same retention, monitoring, and access control settings. The developer can specify the Amazon Resource Name (ARN) of the log group for the API stage, which instructs API Gateway to send the logs to the specified log group. The developer can then examine the logs to determine the cause of the HTTP 400 response errors. Option A is not optimal because it will create an Amazon Kinesis Data Firehose delivery stream to receive API call logs from API Gateway, which may introduce additional costs and complexity for delivering and processing streaming data. Option B is not optimal because it will turn on AWS CloudTrail Insights and create a trail, which is a feature that helps identify and troubleshoot unusual API activity or operational issues, not HTTP response errors. Option C is not optimal because it will turn on AWS X-Ray for the API stage, which is a service that helps analyze and debug distributed applications, not HTTP response errors.

References: [Setting Up CloudWatch Logging for a REST API], [CloudWatch Logs Concepts]

S3Bucket and S3Key: These properties in a CloudFormation AWS::Lambda::Function resource specify the location of the function's code in S3.

Least Development Effort: This solution minimizes code changes, relying on CloudFormation to reference the existing S3 deployment package.

References:

AWS::Lambda::Function Resource https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-lambda-function.html

SNS as a Fan-out Mechanism: SNS is perfect for triggering multiple actions from a single event (here, the image upload).

Workflow:

SNS Topic: Create an SNS topic that will be the central notification point.

S3 Event Notification: Configure the S3 bucket to send 'Object Created' event notifications to the SNS topic.

Lambda Subscription: Subscribe your thumbnail-creating Lambda function to the SNS topic.

Email Subscription: Subscribe an email address to the SNS topic to trigger notifications.

References:

S3 Event Notifications: https://docs.aws.amazon.com/AmazonS3/latest/userguide/EventNotifications.html

SNS Subscriptions: https://docs.aws.amazon.com/sns/latest/dg/SNSMobilePush.html

This solution will ensure that the repository package’s unit tests run in the new deployment environment with the least overhead because it uses AWS CodeBuild to build and test the code in a fully managed service, and AWS CodePipeline to orchestrate the deployment stages and actions. Option A is not optimal because it will use AWS CodeCommit instead of AWS CodeBuild, which is a source control service, not a build and test service. Option C is not optimal because it will use AWS CodeDeploy instead of AWS CodeBuild, which is a deployment service, not a build and test service. Option D is not optimal because it will add an action to the source stage instead of creating a new stage, which will not follow the best practice of separating different deployment phases.

References: AWS CodeBuild, AWS CodePipeline

AWS STS AssumeRole: The central operation for assuming temporary security credentials, commonly used for cross-account access.

MFA Integration: The AssumeRole call can include MFA information to enforce multi-factor authentication.

Credentials for S3 Access: The returned temporary credentials would provide the necessary permissions to access the S3 bucket in the other account.

References:

AWS STS AssumeRole Documentation: https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html

Why Option C is Correct:Ensuring that pipeline artifacts are encrypted with a customer-managed AWS KMS key involves configuring the S3 bucket policy to require encryption. This policy ensures all objects uploaded to the bucket are encrypted with the specified KMS key.

Why Other Options are Incorrect:

Option A: A gateway endpoint improves S3 access efficiency but does not enforce encryption.

Option B: Encrypting CloudWatch Logs is unrelated to securing pipeline artifacts.

Option D: Configuring SNS for encryption does not affect the artifacts stored in the S3 bucket.

AWS Documentation References:

Using Server-Side Encryption with S3 Bucket Policies

DynamoDB TTL (Time-to-Live): A native feature that automatically deletes items after a specified expiration time.

Efficiency: Eliminates the need for scheduled deletion jobs, optimizing write throughput by avoiding potential throttling conflicts.

Seamless Integration: TTL works directly within DynamoDB, requiring minimal development overhead.

References:

DynamoDB TTL Documentation: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html

DynamoDB Accelerator (DAX) provides a managed caching layer specifically optimized for DynamoDB, reducing latency for repeated read requests.

Why Option A is Correct:

Purpose-Built: DAX is designed for DynamoDB, enabling sub-millisecond response times for frequently accessed items.

Operational Efficiency: No need for additional application-level caching logic.

Why Not Other Options:

Option B: Auto Scaling increases capacity but does not address repetitive reads.

Option C: API Gateway caching helps reduce request processing time but does not optimize DynamoDB reads.

Option D: ElastiCache is a general-purpose cache, adding unnecessary complexity for DynamoDB use cases.

References:

DynamoDB Accelerator (DAX)

AWS Systems Manager Parameter Store is a service that provides secure, hierarchical storage for configuration data and secrets. Parameter Store supports SecureString parameters, which are encrypted using AWS Key Management Service (AWS KMS) keys. SecureString parameters can be used to store license keys in AWS and retrieve them securely from automation scripts that run in EC2 instances or CloudFormation stacks. Parameter Store is a cost-effective solution because it does not charge for storing parameters or API calls. Reference: Working with Systems Manager parameters

This solution meets the requirements because it uses a PostgreSQL-compatible database that can secure and regularly rotate database credentials without requiring additional programming overhead. Amazon Aurora PostgreSQL is a relational database service that is compatible with PostgreSQL and offers high performance, availability, and scalability. AWS Secrets Manager is a service that helps you protect secrets needed to access your applications, services, and IT resources. You can store database credentials in AWS Secrets Manager and use them to access your Aurora PostgreSQL database. You can also enable automatic rotation of your secrets according to a schedule or an event. AWS Secrets Manager handles the complexity of rotating secrets for you, such as generating new passwords and updating your database with the new credentials. Using Amazon DynamoDB for the database will not meet the requirements because it is a NoSQL database that is not compatible with PostgreSQL. Using AWS Systems Manager Parameter Store for storing and rotating database credentials will require additional programming overhead to integrate with your database.

This solution will meet the requirements by using AWS Config to monitor and evaluate whether Secrets Manager secrets are unused or have been deleted, based on specified time periods. The secrets manager-secret-unused managed rule is a predefined rule that checks whether Secrets Manager secrets have been rotated within a specified number of days or have been deleted within a specified number of days after last accessed date. The Amazon EventBridge rule will trigger a notification when the AWS Config managed rule is met, alerting the developer about unused secrets that can be removed without causing application downtime. Option A is not optimal because it will use AWS CloudTrail log file delivery to an Amazon S3 bucket, which will incur additional costs and complexity for storing and analyzing log files that may not contain relevant information about secret usage. Option C is not optimal because it will deactivate the application secrets and monitor the application error logs temporarily, which will cause application downtime and potential data loss. Option D is not optimal because it will use AWS X-Ray to trace secret usage, which will introduce additional overhead and latency for instrumenting and sampling requests that may not be related to secret usage.

References: [AWS Config Managed Rules], [Amazon EventBridge]

This solution allows the CloudFormation service role to access the S3 bucket from any account, as long as it has the S3 GetObject permission. The bucket policy grants access to any principal with the GetObject permission, which is the least privilege needed to deploy the Lambda code. This is more secure than granting ListBucket permission, which is not required for deploying Lambda code, or using a service-based link, which is not supported for Lambda functions.

DynamoDB Streams: Capture near real-time changes to DynamoDB tables, triggering downstream actions.

Lambda for Processing: Lambda functions provide a serverless way to execute code in response to events like DynamoDB Stream updates.

Minimal Code Changes: This solution requires the least modifications to the existing application.

References:

DynamoDB Streams: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Streams.html

AWS Lambda: https://aws.amazon.com/lambda/

This solution will meet the requirements by allowing the developer to control what data is sent to X-Ray and CloudWatch from the application code. The developer can filter out any PII from the trace messages before sending them to X-Ray and CloudWatch, ensuring that no PII goes outside of the EC2 instances. Option B is not optimal because it will automatically instrument all incoming and outgoing requests from the application, which may include PII in the trace messages. Option C is not optimal because it will require additional services and costs to use Amazon Macie and AWS Lambda, which may not be able to detect and hide all PII from the trace messages. Option D is not optimal because it will use Open Telemetry instead of X-Ray, which may not be compatible with CloudWatch and other AWS services.

References: [AWS X-Ray SDKs]

This solution will meet the requirements by creating an advanced parameter in AWS Systems Manager Parameter Store, which is a secure and scalable service for storing and managing configuration data and secrets. The advanced parameter allows setting expiration and expiration notification policy types, which enable specifying an expiration date and time for the configuration and receiving notifications before the configuration expires. The Lambda code will be refactored to load the Root CA Cert from the parameter store and modify the runtime trust store outside the Lambda function handler, which will improve performance and reduce latency by avoiding repeated calls to Parameter Store and trust store modifications for each invocation of the Lambda function. Option A is not optimal because it will create a standard parameter in AWS Systems Manager Parameter Store, which does not support expiration and expiration notification policy types. Option B is not optimal because it will create a secret access key and access key ID with permission to access the S3 bucket, which will introduce additional security risks and complexity for storing and managing credentials. Option D is not optimal because it will create a Docker container from Node.js base image to invoke Lambda functions, which will incur additional costs and overhead for creating and running Docker containers.

References: AWS Systems Manager Parameter Store, [Using SSL/TLS to Encrypt a Connection to a DB Instance]

Comprehensive and Detailed Step-by-Step Explanation:

Customer Managed Keys (CMK) for Granular Control (Option B):

Customer-managed KMS keys are required to meet the security policy requirement of team-specific control over KMS key rotation. Each team can manage the lifecycle of its own key.

The kms:Decrypt permission allows the Lambda function execution roles to decrypt the environment variables during runtime.

This solution adheres to the principle of least privilege and satisfies the need for team-specific key control.

Why Other Options Are Incorrect:

Option A: AWS-managed keys cannot provide team-specific control or support the custom rotation policy required by the teams.

Option C: Adding kms:CreateGrant and kms:Encrypt permissions to Lambda roles is unnecessary for this scenario. The key usage is limited to decryption at runtime.

Option D: AWS-managed keys still lack team-specific control, and adding kms:CreateGrant and kms:Encrypt is redundant.

References:

AWS Lambda Environment Variables

AWS Key Management Service Documentation

Secrets Management: AWS Secrets Manager is designed specifically for storing and managing sensitive credentials.

Built-in Rotation: Secrets Manager provides automatic secret rotation functionality, enhancing security posture significantly.

IAM Integration: IAM policies and roles grant fine-grained access to ECS Fargate, ensuring the principle of least privilege.

Reduced Overhead: This solution centralizes secrets management and automates rotation, reducing operational overhead compared to the other options.

References:

AWS Secrets Manager: https://aws.amazon.com/secrets-manager/

Secrets Manager Rotation: https://docs.aws.amazon.com/secretsmanager/latest/userguide/rotating-secrets.html

IAM for Secrets Manager: https://docs.aws.amazon.com/secretsmanager/latest/userguide/auth-and-access_iam-policies.html

Comprehensive and Detailed Step-by-Step Explanation:To ensure successful software deployments using AWS CodeDeploy, the application specification file (appspec.yml or appspec.json) must adhere to specific requirements:

File Format Requirement (Option B):

The appspec.yml file is a YAML-formatted file required for defining deployment actions and file locations. This is the modern and recommended format for application specification files.

It can also be a JSON-formatted file named appspec.json, but YAML is most commonly used and accepted.

File Placement Requirement (Option D):

The application specification file must reside in the root directory of the application's source code. This is necessary so that AWS CodeDeploy can detect and use the file during deployment.

Incorrect Options:

Option A: While a JSON-formatted file (appspec.json) is valid, this is not a mandatory requirement. YAML is also acceptable, and this option does not account for it.

Option C: The application specification file is not required to be stored in AWS CodeBuild; it must be included in the source code's directory structure.

Option E: The application specification file does not need to be stored in Amazon S3. S3 is commonly used for application artifacts, but the appspec.yml or appspec.json file must exist within the deployment package or source code root.

References:

AWS CodeDeploy User Guide: Application Specification File

This solution allows the fastest response for the query because it enables the query to use a single partition key value (the Product ID) and a range of sort key values (the Product Rating) to find the matching items. A global secondary index (GSI) is an index that has a partition key and an optional sort key that are different from those on the base table. A GSI can be created at any time and can be queried or scanned independently of the base table. A local secondary index (LSI) is an index that has the same partition key as the base table, but a different sort key. An LSI can only be created when the base table is created and must be queried together with the base table partition key. Using a GSI with Product ID as the partition key and Review ID as the sort key will not allow the query to use a range of sort key values to find the highest ratings. Using an LSI with Product ID as the partition key and Product Rating as the sort key will not work because Product ID is not the partition key of the base table. Using an LSI with Review ID as the partition key and Product ID as the sort key will not allow the query to use a single partition key value to find the matching items.

The correct answer is A. Add a configuration file in TOML format to group configuration entries to every environment. Add a table for each testing and staging environment. Deploy updates to the environments by using the sam deploy command and the --config-env flag that corresponds to the each environment.

A. Add a configuration file in TOML format to group configuration entries to every environment. Add a table for each testing and staging environment. Deploy updates to the environments by using the sam deploy command and the --config-env flag that corresponds to the each environment. This is correct. This solution will meet the requirements with the least development effort, because it uses a feature of the AWS SAM CLI that supports a project-level configuration file that can be used to configure AWS SAM CLI command parameter values1. The configuration file can have multiple environments, each with its own set of parameter values, such as stack name, region, capabilities, and more2. The developer can use the --config-env option to specify which environment to use when deploying the application3. This way, the developer can avoid creating multiple templates or scripts, or manually overriding parameters for each environment.

B. Create additional AWS SAM templates for each testing and staging environment. Write a custom shell script that uses the sam deploy command and the --template-file flag to deploy updates to the environments. This is incorrect. This solution will not meet the requirements with the least development effort, because it requires creating and maintaining multiple templates and scripts for each environment. This can introduce duplication, inconsistency, and complexity in the deployment process.

C. Create one AWS SAM configuration file that has default parameters. Perform updates to the testing and staging environments by using the —parameter-overrides flag in the AWS SAM CLI and the parameters that the updates will override. This is incorrect. This solution will not meet the requirements with the least development effort, because it requires manually specifying and overriding parameters for each environment every time the developer deploys the application. This can be error-prone, tedious, and inefficient.

D. Use the existing AWS SAM template. Add additional parameters to configure specific attributes for the serverless function and database table resources that are in each environment. Deploy updates to the testing and staging environments by using the sam deploy command. This is incorrect. This solution will not meet the requirements with the least development effort, because it requires modifying the existing template and adding complexity to the resource definitions for each environment. This can also make it difficult to manage and track changes across different environments.

References:

1: AWS SAM CLI configuration file - AWS Serverless Application Model

2: Configuration file basics - AWS Serverless Application Model

3: Specify a configuration file - AWS Serverless Application Model

Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer References:

1. Understanding the Use Case:

The API needs to:

Generate responses without backend integrations: This indicates the use of mock responses for testing.

Be used by multiple internal teams during development.

Minimize operational overhead.

2. Key Features of Amazon API Gateway:

REST APIs: Fully managed API Gateway option that supports advanced capabilities like mock integrations, request/response transformation, and more.

HTTP APIs: Lightweight option for building APIs quickly. It supports fewer features but has lower operational complexity and cost.

Mock Integration: Allows API Gateway to return pre-defined responses without requiring backend integration.

3. Explanation of the Options:

Option A:"Create an Amazon API Gateway REST API. Set up a proxy resource that has the HTTP proxy integration type."A proxy integration requires a backend service for handling requests. This does not meet the requirement of "no backend integrations."

Option B:"Create an Amazon API Gateway HTTP API. Provision a VPC link, and set up a private integration on the API to connect to a VPC."This requires setting up a VPC and provisioning resources, which increases operational overhead and is unnecessary for this use case.

Option C:"Create an Amazon API Gateway HTTP API. Enable mock integration on the method of the API resource."While HTTP APIs can enable mock integrations, they have limited support for advanced features compared to REST APIs, such as detailed request/response customization. REST APIs are better suited for development environments requiring mock responses.

Option D:"Create an Amazon API Gateway REST API. Enable mock integration on the method of the API resource."This is the correct answer. REST APIs with mock integration allow defining pre-configured responses directly within API Gateway, making them ideal for scenarios where backend services are unavailable. It provides flexibility for testing while minimizing operational overhead.

4. Implementation Steps:

To enable mock integration with REST API:

Create a REST API in API Gateway:

Open the API Gateway Console.

Choose Create API > REST API.

Define the API Resource and Methods:

Add a resource and method (e.g., GET or POST).

Set Up Mock Integration:

Select the method, and in the Integration Type, choose Mock Integration.

Configure the Mock Response:

Define a 200 OK response with the desired response body and headers.

Deploy the API:

Deploy the API to a stage (e.g., dev) to make it accessible.

5. Why REST API Over HTTP API?

REST APIs support detailed request/response transformations and robust mock integration features, which are ideal for development and testing scenarios.

While HTTP APIs offer lower cost and simplicity, they lack some advanced features required for fine-tuned mock integrations.

References:

Amazon API Gateway REST API Features

Mock Integration in API Gateway

Comparison of REST and HTTP APIs in API Gateway

AWS API Gateway Pricing

This solution will meet the requirements by ensuring that all traffic between users and CloudFront, and all traffic between CloudFront and the web application, are encrypted using HTTPS protocol. The Origin Protocol Policy determines how CloudFront communicates with the origin server (the web application), and setting it to “HTTPS Only” will force CloudFront to use HTTPS for every request to the origin server. The Viewer Protocol Policy determines how CloudFront responds to HTTP or HTTPS requests from users, and setting it to “HTTPS Only” or “Redirect HTTP to HTTPS” will force CloudFront to use HTTPS for every response to users. Option A is not optimal because it will use AWS KMS to encrypt traffic between CloudFront and the web application, which is not necessary or supported by CloudFront. Option C is not optimal because it will set the origin’s HTTP port to 443, which is incorrect as port 443 is used for HTTPS protocol, not HTTP protocol. Option E is not optimal because it will enable the CloudFront option Restrict Viewer Access, which is used for controlling access to private content using signed URLs or signed cookies, not for encrypting traffic.

References: [Using HTTPS with CloudFront], [Restricting Access to Amazon S3 Content by Using an Origin Access Identity]

This solution will meet the requirements by using AWS X-Ray to enable tracing of application requests to debug performance issues in the code. AWS X-Ray is a service that collects data about requests that the applications serve, and provides tools to view, filter, and gain insights into that data. The developer can install the AWS X-Ray daemon on the EC2 instances, which is a software that listens for traffic on UDP port 2000, gathers raw segment data, and relays it to the X-Ray API. The developer can also install and configure the AWS X-Ray SDK for Python in the application, which is a library that enables instrumenting Python code to generate and send trace data to the X-Ray daemon. Option A is not optimal because it will install the Amazon CloudWatch agent on the EC2 instances, which is a software that collects metrics and logs from EC2 instances and on-premises servers, not application performance data. Option C is not optimal because it will configure the application to write JSON-formatted logs to /var/log/cloudwatch, which is not a valid path or destination for CloudWatch logs. Option D is not optimal because it will configure the application to write trace data to /var/log/xray, which is also not a valid path or destination for X-Ray trace data.

References: [AWS X-Ray], [Running the X-Ray Daemon on Amazon EC2]

The solution that will meet the requirements is to add a new stage to the pipeline. Use AWS CodeBuild as the provider. Add the new stage before the stage that deploys code revisions to the test environment. Write a buildspec that fails the CodeBuild stage if any test does not pass. Use the test reports feature of CodeBuild to integrate the report with the CodeBuild console. View the test results in CodeBuild. Resolve any issues. This way, the developer can run the unit tests automatically during the CI/CD process and catch any bugs before deploying to the test environment. The developer can also use the test reports feature of CodeBuild to view and analyze the test results in a graphical interface. The other options either involve running the tests manually, running them after deployment, or using a different provider that requires additional configuration and integration.

This solution will meet the requirements by using AWS X-Ray to analyze and debug the performance issues with the distributed Lambda applications. AWS X-Ray is a service that collects data about requests that the applications serve, and provides tools to view, filter, and gain insights into that data. The developer can use AWS X-Ray to identify the root cause of the performance issues by examining the segments and errors that show the details of each request and the components that make up the applications. Option A is not optimal because it will use logging statements and Amazon CloudWatch, which may not provide enough information or visibility into the distributed applications. Option B is not optimal because it will use AWS CloudTrail, which is a service that records API calls and events for AWS services, not application performance data. Option D is not optimal because it will use Amazon Inspector, which is a service that helps improve the security and compliance of applications on Amazon EC2 instances, not Lambda functions.

References: AWS X-Ray, Using AWS X-Ray with AWS Lambda

Visibility Timeout: When an SQS message is processed by a consumer (here, the Lambda function), it's temporarily hidden from other consumers. Visibility timeout controls this duration.

How It Helps:

Increase the visibility timeout beyond the maximum processing time your Lambda might typically take for long videos.

This prevents the message from reappearing in the queue while Lambda is still working, avoiding premature timeouts.

References:

SQS Visibility Timeout: https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-visibility-timeout.html

Amazon Cognito user pools support Lambda triggers, which are custom functions that can be executed at various stages of the user pool workflow. A post authentication Lambda trigger can be used to perform custom actions after a user is authenticated, such as sending an email notification. Amazon SES is a cloud-based email sending service that can be used to send transactional or marketing emails. A Lambda function can use the Amazon SES API to send an email to the user’s email address after the user logs in successfully. Reference: Post authentication Lambda trigger

The command that will meet the requirements is sam sync -watch. This command enables AWS SAM Accelerate mode, which allows the developer to only redeploy the Lambda functions that have changed. The -watch flag enables file watching, which automatically detects changes in the source code and triggers a redeployment. The other commands either do not enable AWS SAM Accelerate mode, or do not redeploy the Lambda functions automatically.

Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer References:

1. Understanding the Use Case:

The goal is to store objects in an S3 bucket while optimizing storage costs. The key conditions are:

Objects are accessed infrequently after 1 week.

Objects must remain immediately accessible at all times.

2. AWS S3 Storage Classes Overview:

Amazon S3 offers various storage classes, each optimized for specific use cases:

S3 Standard: Best for frequently accessed data with low latency and high throughput needs.

S3 Standard-Infrequent Access (S3 Standard-IA): Optimized for infrequently accessed data but requires the same availability and immediate access as Standard storage. It provides lower storage costs but incurs retrieval charges.

S3 Glacier Flexible Retrieval (formerly S3 Glacier): Designed for archival data with retrieval latency ranging from minutes to hours. This does not meet the requirement for "immediate access."

S3 Glacier Deep Archive: Lowest-cost storage, suitable for rarely accessed data with retrieval times of hours.

3. Explanation of the Options:

Option A:"Create an S3 Lifecycle rule to expire objects after 7 days."Expiring objects after 7 days deletes them permanently, which does not fulfill the requirement of retaining the objects for later infrequent access.

Option B:"Create an S3 Lifecycle rule to transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 7 days."This is the correct solution. S3 Standard-IA is ideal for objects accessed infrequently but still need to be available immediately. Transitioning objects to this storage class reduces storage costs while maintaining availability and low latency.

Option C:"Create an S3 Lifecycle rule to transition objects to S3 Glacier Flexible Retrieval after 7 days."S3 Glacier Flexible Retrieval is a low-cost archival solution. However, it does not provide immediate access as retrieval requires minutes to hours. This option does not meet the requirement.

Option D:"Create an S3 Lifecycle rule to delete objects that have delete markers."This option is irrelevant to the given use case, as it addresses versioning cleanup, which is not enabled in the described S3 bucket.

4. Implementation Steps for Option B:

To transition objects to S3 Standard-IA after 7 days:

Navigate to the S3 Console:

Sign in to the AWS Management Console and open the S3 service.

Select the Target Bucket:

Choose the bucket where the objects are stored.

Set Up a Lifecycle Rule:

Go to the Management tab.

Under Lifecycle Rules, click Create lifecycle rule.

Define the Rule Name and Scope:

Provide a descriptive name for the rule.

Specify whether the rule applies to the entire bucket or a subset of objects (using a prefix or tag filter).

Configure Transitions:

Choose Add transition.

Specify that objects should transition to S3 Standard-IA after 7 days.

Review and Save the Rule:

Review the rule configuration and click Save.

5. Cost Optimization Benefits:

Transitioning to S3 Standard-IA results in cost savings as it offers:

Lower storage costs compared to S3 Standard.

Immediate access to objects when required.However, remember that there is a retrieval cost associated with S3 Standard-IA, so it is best suited for data with low retrieval frequency.

References:

Amazon S3 Lifecycle Configuration Guide

Amazon S3 Storage Classes

AWS S3 Pricing

AWS Documentation on S3 Standard-IA

IAM Roles for EC2 (A): The most secure way to provide AWS permissions from EC2.

Create a role with a policy allowing s3:GetObject on the specific bucket.

Attach the role to an instance profile and associate that profile with your instances.

Pre-signed URLs (C): Temporary, authenticated URLs for specific S3 actions.

Modify the app to use the AWS SDK to call GeneratePresignedUrl.

Embed these URLs when a user is properly logged in, allowing download access.

References:

IAM Roles for EC2: https://docs. aws.amazon.com/IAM/latest/UserGuide/id_roles_use_switch-role-ec2.html

Generating Presigned URLs: https://docs.aws.amazon.com/AmazonS3/latest/userguide/ShareObjectPreSignedURL.htm

This solution will ensure that all orders and updates are stored to Amazon S3 with the least development effort because it uses DynamoDB Streams to capture changes in the DynamoDB table and trigger a Lambda function to write those changes to the S3 bucket. This way, the original Lambda function and API Gateway API endpoint do not need to be modified, and no additional services are required. Option A is not optimal because it will require more development effort to create a new Lambda function and a new API Gateway API endpoint, and to modify the original Lambda function to post updates to the new API endpoint. Option B is not optimal because it will introduce additional costs and complexity to use Amazon Kinesis Data Streams to create a new data stream, and to modify the Lambda function to publish orders to the data stream. Option D is not optimal because it will require more development effort to modify the Lambda function to publish to a new Amazon SNS topic, and to create and subscribe a new Lambda function to the topic.

References: Using DynamoDB Streams, Using AWS Lambda with Amazon S3

EventBridge (formerly CloudWatch Events) is the ideal service for real-time event monitoring.

CloudTrail logs IAM role creation.

EventBridge rules can filter CloudTrail events and trigger SNS notifications instantly.

The immutable deployment method is the best option for this scenario, because it meets the requirements of maintaining full capacity, avoiding service interruption, and minimizing the cost of additional resources.

The immutable deployment method creates a new set of instances in a separate Auto Scaling group and deploys the new version of the application to them. Then, it swaps the new instances with the old ones and terminates the old instances. This way, the application maintains full capacity during the deployment and avoids any downtime. The cost of additional resources is also minimized, because the new instances are only created for a short time and then replaced by the old ones.

The other deployment methods do not meet all the requirements:

The all at once method deploys the new version to all instances simultaneously, which causes a short period of downtime and reduced capacity.

The rolling with additional batch method deploys the new version in batches, but for the first batch it creates new instances instead of using the existing ones. This increases the cost of additional resources and reduces the capacity of the original environment.

The blue/green method creates a new environment with a new set of instances and deploys the new version to them. Then, it swaps the URLs between the old and new environments. This method maintains full capacity and avoids service interruption, but it also increases the cost of additional resources significantly, because it duplicates the entire environment.

Amazon ElastiCache for Redis: An in-memory data store known for extremely low latency, ideal for caching frequently accessed, complex data.

Write-Through Caching: Ensures that data is always consistent between the cache and the database. Writes go to both Redis and RDS.

Performance Gains: Redis handles reads with minimal latency, offloading the RDS database and improving the application's responsiveness.

References:

Amazon ElastiCache for Redis Documentation: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/

Caching Strategies: https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/Strategies.html

Problem: Directory access without file names fails.

S3 Static Website Hosting:

Configuring S3 as a static website enables automatic serving of index.html for directory requests.

Bucket policies ensure correct access permissions.

Updating the CloudFront origin simplifies routing.

Avoiding Public Exposure: The S3 website endpoint allows CloudFront to access content without making the bucket public.

References:

S3 Static Website Hosting: https://docs.aws.amazon.com/AmazonS3/latest/userguide/WebsiteHosting.html

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_examples_lambda-access-dynamodb.html

Amazon DynamoDB is a fully managed NoSQL database service that can store and retrieve any amount of data with high availability and performance. DynamoDB can handle concurrent requests from multiple IoT devices without throttling or data loss. To prevent duplicate requests from causing inconsistencies or data loss, the Lambda function can use DynamoDB conditional writes to check if the unique identifier for each request already exists in the table before processing the request. If the identifier exists, the function can skip or abort the request; otherwise, it can process the request and store the identifier in the table. Reference: Using conditional writes

The correct answer is C. Use an AWS Lambda function that is invoked by an Amazon EventBridge scheduled event.

C. Use an AWS Lambda function that is invoked by an Amazon EventBridge scheduled event. This is correct. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. Lambda runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, and logging1. Amazon EventBridge is a serverless event bus service that enables you to connect your applications with data from a variety of sources2. EventBridge can create rules that run on a schedule, either at regular intervals or at specific times and dates, and invoke targets such as Lambda functions3. This solution meets the requirements of creating a small application that makes the same API call once each day at a designated time, without requiring any infrastructure in the AWS Cloud or any operational overhead.

A. Use a Kubernetes cron job that runs on Amazon Elastic Kubernetes Service (Amazon EKS). This is incorrect. Amazon EKS is a fully managed Kubernetes service that allows you to run containerized applications on AWS4. Kubernetes cron jobs are tasks that run periodically on a given schedule5. This solution could meet the functional requirements of creating a small application that makes the same API call once each day at a designated time, but it would not be the most operationally efficient manner. The company would need to provision and manage an EKS cluster, which would incur additional costs and complexity.

B. Use an Amazon Linux crontab scheduled job that runs on Amazon EC2. This is incorrect. Amazon EC2 is a web service that provides secure, resizable compute capacity in the cloud6. Crontab is a Linux utility that allows you to schedule commands or scripts to run automatically at a specified time or date7. This solution could meet the functional requirements of creating a small application that makes the same API call once each day at a designated time, but it would not be the most operationally efficient manner. The company would need to provision and manage an EC2 instance, which would incur additional costs and complexity.

D. Use an AWS Batch job that is submitted to an AWS Batch job queue. This is incorrect. AWS Batch enables you to run batch computing workloads on the AWS Cloud8. Batch jobs are units of work that can be submitted to job queues, where they are executed in parallel or sequentially on compute environments9. This solution could meet the functional requirements of creating a small application that makes the same API call once each day at a designated time, but it would not be the most operationally efficient manner. The company would need to configure and manage an AWS Batch environment, which would incur additional costs and complexity.

References:

1: What is AWS Lambda? - AWS Lambda

2: What is Amazon EventBridge? - Amazon EventBridge

3: Creating an Amazon EventBridge rule that runs on a schedule - Amazon EventBridge

4: What is Amazon EKS? - Amazon EKS

5: CronJob - Kubernetes

6: What is Amazon EC2? - Amazon EC2

7: Crontab in Linux with 20 Useful Examples to Schedule Jobs - Tecmint

8: What is AWS Batch? - AWS Batch

9: Jobs - AWS Batch

The sam local start-api subcommand allows you to run your serverless application locally for quick development and testing1. It creates a local HTTP server that acts as a proxy for API Gateway and invokes your Lambda functions based on the AWS SAM template1. You can use the sam local start-api subcommand to test your REST API locally by sending HTTP requests to the local endpoint1.

The Immutable deployment method is the best choice when a company requires minimal downtime and automatic rollback in case of a failure. Here's why:

In Immutable deployments, a new set of instances is launched with the updated version. These instances are tested and validated before they replace the old instances. This ensures zero downtime and immediate rollback if the deployment fails.

All at once (A) causes downtime because the update replaces all instances simultaneously.

Rolling deployments (B) update a few instances at a time, but if a failure occurs midway, downtime or partial unavailability can happen.

Snapshots (C) are not a deployment strategy in Elastic Beanstalk.

Amazon EventBridge: A service that reacts to events from various AWS sources, including S3. Rules define which events trigger actions (like invoking Lambda functions).

S3 Object Created Events: EventBridge can detect these, providing seamless integration for automated CSV processing.

S3 Lifecycle Rules: Allow for actions based on object age or prefixes. These can directly trigger Lambda functions for file processing.

References:

Amazon EventBridge Documentation: https://docs.aws.amazon.com/eventbridge/

Working with S3 Event Notifications: https://docs.aws.amazon.com/AmazonS3/latest/userguide/EventNotifications.html

S3 Lifecycle Configuration: https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lifecycle-mgmt.html

Step 1: Understanding the Requirements

Gradual Traffic Shift: Test the new version by routing only 10% of production traffic to it.

Lambda Deployment: Use versioning and aliases to manage Lambda function updates.

Step 2: Solution Analysis

Option A:

Publishing a new version creates an immutable version of the Lambda function with the updated code.

This is a prerequisite for deploying changes using weighted aliases.

Correct option.

Option B:

API Gateway stages are not used for weighted routing; they represent environments like "dev" or "prod."

Weighted routing is implemented using Lambda aliases, not API Gateway stages.

Not suitable.

Option C:

Lambda aliases allow traffic to be split between versions using weighted routing.

Assign a 90% weight to the old version and 10% to the new version to implement the gradual rollout.

Correct option.

Option D:

Network Load Balancers are not suitable for managing Lambda function traffic directly.

Not applicable.

Option E:

Route 53 routing policies apply at the DNS level and are not designed for Lambda version management.

Not suitable.

Step 3: Implementation Steps

Publish a New Version:

Publish the updated Lambda function code as a new version.

Create an Alias and Configure Weighted Routing:

Create an alias (e.g., prod) and associate it with both the old and new versions.

Set weights for traffic distribution:

aws lambda update-alias --function-name my-function \

--name prod --routing-config '{"AdditionalVersionWeights": {"2": 0.1}}'

AWS Developer References:

Lambda Function Aliases

Weighted Traffic Routing with Lambda

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/access-control-overview.html#access-control-resource-ownership

Problem: CloudFormation can't find the custom AMI in the target region (us-west-1) because AMIs are region-specific.

Copying AMIs:

AMIs can be copied across regions, maintaining their configuration.

This approach minimizes operational overhead as the existing CloudFormation template can be reused with a minor update.

Updating the Template:

Modify the CloudFormation template in us-west-1 to reference the newly copied AMI's ID in that region.

References:

Copying AMIs: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/CopyingAMIs.html

CloudFormation Templates and AMIs: [invalid URL removed]

Step 1: Understanding the Problem

Processing in Batches: The application must process records in groups.

Sequential Processing: Each record in the batch must be processed before writing to Aurora.

Solution Goals: Use services that support ordered, batched processing and integrate with Aurora.

Step 2: Solution Analysis

Option A:

Amazon Kinesis Data Streams supports ordered data processing.

AWS Lambda can process batches of records via event source mapping with MaximumBatchingWindowInSeconds for timing control.

Configuring the batching window ensures efficient processing and compliance with the workflow.

Correct Option.

Option B:

Amazon SQS is not designed for streaming; it provides reliable, unordered message delivery.

Setting MaximumBatchingWindowInSeconds to 0 disables batching, which is contrary to the requirement.

Not suitable.

Option C:

Amazon MSK provides Kafka-based streaming but requires custom EC2-based processing.

This increases system complexity and operational overhead.

Not ideal for serverless requirements.

Option D:

DynamoDB Streams is event-driven but lacks strong native integration for batch ordering.

Using ECS adds unnecessary complexity.

Not suitable.

Step 3: Implementation Steps for Option A

Set up Kinesis Data Stream:

Configure shards based on the expected throughput.

Configure Lambda with Event Source Mapping:

Enable Kinesis as the event source for Lambda.

Set MaximumBatchingWindowInSeconds to 300 to accumulate data for processing.

Example:

{

"EventSourceArn": "arn:aws:kinesis:region:account-id:stream/stream-name",

"BatchSize": 100,

"MaximumBatchingWindowInSeconds": 300

}

Write Processed Data to Aurora:

Use AWS RDS Data API for efficient database operations from Lambda.

AWS Developer References:

Amazon Kinesis Data Streams Developer Guide

AWS Lambda Event Source Mapping

Batch Processing with Lambda

CloudFormation and Dynamic References: The DefinitionSubstitutions property in CloudFormation allows you to pass values into Step Functions state machines at runtime.

Cost-Effectiveness: This solution is cost-effective as it leverages CloudFormation's built-in capabilities, avoiding the need for additional services like Secrets Manager or AppConfig.

References:

AWS Step Functions State Machine: https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-stepfunctions-statemachine.html

CloudFormation DefinitionSubstitutions: https://github.com/aws-cloudformation/aws-cloudformation-resource-providers-stepfunctions/issues/14

The Deployment Preference Type property specifies how traffic should be shifted between versions of a Lambda function1. The Canary10Percent10Minutes option means that 10% of the traffic is immediately shifted to the new version, and after 10 minutes, the remaining 90% of the traffic is shifted1. This matches the requirement of shifting 10% of the traffic for the first 10 minutes, and then switching all traffic to the new version.

The AutoPublishAlias property enables AWS SAM to automatically create and update a Lambda alias that points to the latest version of the function1. This is required to use the Deployment Preference Type property1. The alias name can be specified by the developer, and it can be used to invoke the function with the latest code.

Amazon S3 Glacier Instant Retrieval is an archive storage class that delivers the lowest-cost storage for long-lived data that is rarely accessed and requires retrieval in milliseconds. With S3 Glacier Instant Retrieval, you can save up to 68% on storage costs compared to using the S3 Standard-Infrequent Access (S3 Standard-IA) storage class, when your data is accessed once per quarter. https://aws.amazon.com/s3/storage-classes/glacier/instant-retrieval/

Understanding Storage Requirements:

Files are large and infrequently accessed, but need to be available within minutes when requested in the first year.

Long-term (7-year) retention is required.

Cost-effectiveness is a top priority.

Why S3 Glacier Instant Retrieval:

Matches the retrieval requirements (access within minutes).

More cost-effective than S3 Standard for infrequently accessed data.

Simpler to use than traditional Glacier where retrievals take hours.

Why S3 Glacier Deep Archive:

Most cost-effective S3 storage class for long term archival.

Meets the 7-year retention requirement.

S3 Lifecycle Policy:

Automate the transition from Glacier Instant Retrieval to Glacier Deep Archive after one year.

Optimize costs by matching storage classes to access patterns.

References:

Amazon S3 Storage Classes: https://aws.amazon.com/s3/storage-classes/

S3 Glacier Instant Retrieval: [invalid URL removed]

S3 Glacier Deep Archive: [invalid URL removed]

S3 Lifecycle Policies: https://docs.aws.amazon.com/AmazonS3/latest/userguide/object-lifecycle-mgmt.html

This solution will meet the requirements by using a rolling with additional batch deployment method, which deploys the new version of the application to a separate group of instances and then shifts traffic to those instances in batches. This way, the application maintains full capacity and avoids service interruption during deployment, as well as minimizes the cost of additional resources that support the deployment. Option A is not optimal because it will use an all at once deployment method, which deploys the new version of the application to all instances simultaneously, which may cause service interruption or downtime during deployment. Option C is not optimal because it will use a blue/green deployment method, which deploys the new version of the application to a separate environment and then swaps URLs with the original environment, which may incur more costs for additional resources that support the deployment. Option D is not optimal because it will use an immutable deployment method, which deploys the new version of the application to a fresh group of instances and then redirects traffic to those instances, which may also incur more costs for additional resources that support the deployment.

References: AWS Elastic Beanstalk Deployment Policies

Why Option C is Correct:Implementing a connection pool in the Redis client reduces connection overhead and avoids frequent connection establishment, which helps to reduce latency and connection failures.

Why Other Options are Incorrect:

Option A: Exponential backoff retry strategies help with transient failures but do not resolve latency caused by frequent connection establishment.

Option B: Storing scores in application memory adds complexity and risks inconsistency.

Option D: Adding more nodes is unnecessary unless the cluster is under heavy load. Latency due to connection failures is better addressed at the application level.

AWS Documentation References:

Amazon ElastiCache Best Practices

Default SSE in DynamoDB: DynamoDB tables are encrypted at rest by default using an AWS owned key (SSE-S3).

No Additional Action Needed: Creating a table without explicitly specifying a KMS key will use this default encryption.

References:

DynamoDB Server-Side Encryption: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Encryption

Secrets Management: AWS Secrets Manager is designed specifically for storing and managing sensitive credentials.

Environment Isolation: Creating separate secrets for each environment (development, staging, etc.) ensures clear separation and prevents accidental leaks.

Automatic Rotation: Secrets Manager provides built-in rotation capabilities, enhancing security posture.

Versioning: Tracking changes to secrets is essential for auditing and compliance.

References:

AWS Secrets Manager: https://aws.amazon.com/secrets-manager/

Secrets Manager Rotation: https://docs.aws.amazon.com/secretsmanager/latest/userguide/rotating-secrets.html

Amazon EFS: A network file system (NFS) providing shared, scalable storage across multiple Lambda functions and other AWS resources.

Lambda Mounting: EFS file systems can be mounted within Lambda functions to access a shared storage space.

Log Appending: EFS supports appending data to existing files, making it ideal for the log file scenario.

References:

Amazon EFS Documentation: https://docs.aws.amazon.com/efs/

Using Amazon EFS with AWS Lambda: https://docs.aws.amazon.com/lambda/latest/dg/services-efs.html

Why Option C is Correct:SQL Server Always On availability groups require a shared storage solution. Amazon FSx for Windows File Server provides the shared storage necessary to implement Always On availability groups in a highly available configuration.

Why Other Options are Incorrect:

Option A: A single EBS volume cannot provide shared storage for Always On availability groups.

Option B: RDS does not support SQL Server Always On availability groups.

Option D: S3 is not a suitable storage tier for SQL Server database operations.

AWS Documentation References:

Amazon FSx for Windows File Server

Amazon S3 supports server-side encryption, which encrypts data at rest on the server that stores the data. One of the encryption options is SSE-S3, which uses keys managed by S3. To use SSE-S3, the x-amz-server-side-encryption header must be set to AES256 when invoking the PutObject API operation. This instructs S3 to encrypt the object data with SSE-S3 before saving it on disks in its data centers and decrypt it when it is downloaded. Reference: Protecting data using server-side encryption with Amazon S3-managed encryption keys (SSE-S3)

This solution allows the developer to troubleshoot the failure by capturing unprocessed events in a queue for further analysis. Dead Letter Queues (DLQs) are queues that store messages that could not be processed by a service, such as Lambda, for various reasons, such as configuration errors, throttling limits, or permissions issues. The developer can configure DLQs for Lambda functions by sending events to either an Amazon Simple Queue Service (SQS) queue or an Amazon Simple Notification Service (SNS) topic. The developer can then inspect the messages in the queue or topic to identify and fix the root cause of the failure. Configuring AWS CloudTrail logging will not capture invocation failures for asynchronous Lambda invocations, but only record API calls made by or on behalf of Lambda. Configuring Amazon Simple Workflow Service (SWF) or AWS Config will not process any direct unprocessed events, but require additional integration and configuration.

This solution will limit the ability to download a premier content file in the S3 bucket to paid subscribers only because it uses a pre-signed object URL that grants temporary access to an S3 object for a specified duration. The pre-signed object URL can be generated by the company’s website when a paid subscriber requests a download, and can be verified by Amazon S3 using the signature in the URL. Option A is not optimal because it will allow anyone to download the content from the S3 bucket without verifying their subscription status. Option C is not optimal because it will require additional steps and costs to configure multi-factor authentication for accessing the S3 bucket objects, which may not be feasible or user-friendly for paid subscribers. Option D is not optimal because it will not prevent non-paying website visitors from accessing the S3 bucket objects, but only encrypt them at rest.

References: Share an Object with Others, [Using Amazon S3 Pre-Signed URLs]

The UnprocessedKeys element indicates that the BatchGetItem operation did not process all of the requested items in the current response. This can happen if the response size limit is exceeded or if the table’s provisioned throughput is exceeded. To handle this situation, the developer should retry the batch operation with exponential backoff and randomized delay to avoid throttling errors and reduce the load on the table. The developer should also use an AWS SDK to make the requests, as the SDKs automatically retry requests that return UnprocessedKeys.

References:

[BatchGetItem - Amazon DynamoDB]

[Working with Queries and Scans - Amazon DynamoDB]

[Best Practices for Handling DynamoDB Throttling Errors]

TTL for Automatic Deletion: DynamoDB's Time-to-Live effortlessly deletes expired items without manual intervention.

DynamoDB Stream: Captures changes to the table, including deletions of expired items, triggering downstream actions.

Lambda for Processing: A Lambda function connected to the stream provides custom logic for handling the deleted items.

Code Efficiency: This solution leverages native DynamoDB features and stream-based processing, minimizing the need for custom code.

References:

DynamoDB TTL Documentation: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html

DynamoDB Streams Documentation: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Streams.html

HTTP Status Codes: Each HTTP status code has a specific meaning in RESTful APIs.

HTTP 405 (Method Not Allowed): Indicates that the request method (e.g., POST) is not supported for the specified resource.

HTTP 401 (Unauthorized): Represents a failure to authenticate, which is the appropriate response for invalid login credentials.

References:

HTTP Status Codes: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status

Why Option C is Correct:

Amazon ElastiCache (Memcached) provides low-latency, in-memory caching suitable for session storage. It ensures stateless web tier operations and supports the high throughput of 5000 requests per minute.

Why Other Options are Incorrect:

Option A: RDS has higher latency compared to in-memory caching solutions like ElastiCache.

Option B: Shared file systems introduce additional complexity and are not ideal for low-latency session data storage.

Option D: DynamoDB has low latency but is less performant than ElastiCache for in-memory session management.

AWS Documentation References:

Amazon ElastiCache for Session State Management

Why Option B is Correct:The RetainExceptOnCreate deletion policy ensures that the IAM user is retained after successful stack creation but is deleted if the stack creation fails or rolls back. This meets both requirements.

Why Other Options are Incorrect:

Option A: The Retain policy retains the resource regardless of stack status and does not delete the IAM user upon rollback.

Option C: Updating the service role for termination protection does not address the specific deletion behavior for the IAM user.

Option D: Stack policy controls updates, not resource deletion behavior during rollbacks.

AWS Documentation References:

CloudFormation DeletionPolicy Attribute

This solution is the most efficient method to grant users access to AWS resources without requiring them to log in. Amazon Cognito is a service that provides user sign-up, sign-in, and access control for web and mobile applications. Amazon Cognito identity pools support both authenticated and unauthenticated users. Unauthenticated users receive access to your AWS resources even if they aren’t logged in with any of your identity providers (IdPs). You can use Amazon Cognito to associate unauthenticated users with an IAM role that has limited access to resources, such as Amazon S3 buckets or DynamoDB tables. This degree of access is useful to display content to users before they log in or to allow them to perform certain actions without signing up. Using an identity provider to securely authenticate with the application will require users to log in, which does not meet the requirement. Creating an AWS Lambda function to create an IAM user when a user accesses the application will incur unnecessary costs and complexity, and may pose security risks if not implemented properly. Creating credentials using AWS KMS and applying them to users when using the application will also incur unnecessary costs and complexity, and may not provide fine-grained access control for resources.

AWS CodeCommit is a service that provides fully managed source control for hosting secure and scalable private Git repositories. The development team can use CodeCommit to store the program code and prepare for the CI/CD pipeline. CodeCommit integrates with other AWS services such as CodePipeline, CodeBuild, and CodeDeploy to automate the code build and deployment process.

References:

[What Is AWS CodeCommit? - AWS CodeCommit]

[AWS CodePipeline - AWS CodeCommit]

Amazon EC2 instances can send the state-change notification events to Amazon EventBridge. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/monitoring-instance-state-changes.html Amazon EventBridge can send and receive events between event buses in AWS accounts. https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-cross-account.html

DynamoDB distributes throughput across partitions based on the hash key. A hot partition (caused by high usage of a specific hash key) can result in a ProvisionedThroughputExceededException, even if overall usage is below the provisioned capacity.

Why Option B is Correct:

Partition-Level Limits: Each partition has a limit of 3,000 read capacity units or 1,000 write capacity units per second.

Hot Partition: Excessive use of a single hash key can overwhelm its partition.

Why Not Other Options:

Option A: DynamoDB storage size does not affect throughput.

Option C: Provisioned scaling operations are unrelated to throughput errors.

Option D: Sort keys do not impact partition-level throughput.

References:

DynamoDB Partition Key Design Best Practices

Amazon CloudWatch is the service that collects and stores logs from AWS Lambda functions. The developer can use CloudWatch Logs Insights to query and analyze the logs for errors and metrics. Option A is not correct because Amazon S3 is a storage service that does not store Lambda function logs. Option B is not correct because AWS CloudTrail is a service that records API calls and events for AWS services, not Lambda function logs. Option D is not correct because Amazon DynamoDB is a database service that does not store Lambda function logs.

References: AWS Lambda Monitoring, [CloudWatch Logs Insights]

Comprehensive Detailed Step by Step Explanation with All AWS Developer References:To ensure that only a specific AWS Step Functions state machine (myStateMachine) can assume the service role, you must configure the correct trust policy in AWS IAM.

Trust Policies: Trust policies determine which entities (services or users) are allowed to assume the role. In this case, we want to restrict the trust policy to only allow the specific state machine (myStateMachine) to assume the role.

Using ArnLike: The condition "ArnLike" is used to specify that the SourceArn (which refers to the ARN of the entity assuming the role) must match a specific ARN. Option A specifies the exact ARN of the myStateMachine state machine, ensuring that only this state machine can assume the role.

Option B: This option is incorrect because it uses a wildcard (*) for the account ID, which would allow any state machine in the ap-south-1 region to assume the role, not just the specific one.

Pseudo Parameters: CloudFormation provides pseudo parameters that reference runtime context, including the current AWS Region.

Operational Efficiency: The AWS::Region pseudo parameter offers the most direct and self-contained way to obtain the Region dynamically within the template.

References:

CloudFormation Pseudo Parameters: https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/pseudo-parameter-reference.html

AWS Serverless Application Model (AWS SAM) is an open-source framework that enables developers to build and deploy serverless applications on AWS. AWS SAM uses a template specification that extends AWS CloudFormation to simplify the definition of serverless resources such as API Gateway, DynamoDB, and Lambda. The developer can use AWS SAM to define serverless resources in YAML and deploy them using the AWS SAM CLI.

References:

[What Is the AWS Serverless Application Model (AWS SAM)? - AWS Serverless Application Model]

[AWS SAM Template Specification - AWS Serverless Application Model]

The GenerateDataKey API returns a data key that is encrypted under a symmetric encryption KMS key that you specify, and a plaintext copy of the same data key1. The data key is a random byte string that can be used with any standard encryption algorithm, such as AES or SM42. The plaintext data key can be used to encrypt or decrypt data outside of AWS KMS, while the encrypted data key can be stored with the encrypted data and later decrypted by AWS KMS1.

In this scenario, the developer needs to use the GenerateDataKey API to encrypt the PDF file so that it can be decrypted later. The developer also needs to use an AWS KMS symmetric customer managed key for encryption. To achieve this, the developer can follow these steps:

Call the GenerateDataKey API with the symmetric customer managed key ID and the desired length or specification of the data key. The API will return an encrypted data key and a plaintext data key.

Write the encrypted data key to disk for later use. This will allow the developer to decrypt the data key and the PDF file later by using AWS KMS.

Use the plaintext data key and a symmetric encryption algorithm to encrypt the PDF file. The developer can use any standard encryption library or tool to perform this operation, such as OpenSSL or AWS Encryption SDK.

Discard the plaintext data key from memory as soon as possible after using it. This will prevent unauthorized access or leakage of the data key.