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Amazon Web Services MLS-C01 Dumps

Exam Code:
MLS-C01
Exam Name:
AWS Certified Machine Learning - Specialty
Last Update: Jun 17, 2024
281 Questions with Explanation
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MLS-C01 Exam Details

Format: Multiple choice, Multiple Answer
Exam Type: Specialty
Exam Method: Testing center or online proctored exam
Time: 180 Min
Exam Price: $300 USD
Language: Available in English, Japanese, Korean, and Simplified Chinese

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MLS-C01 FAQs

The AWS MLS-C01 Exam focuses on machine learning and artificial intelligence concepts, including model training, data engineering, algorithm selection, and deployment. It also tests AWS-specific services like SageMaker, Lex, and Rekognition, alongside machine learning security and best practices.

The MLS-C01, unlike other AWS Associate-level exams, specializes in machine learning and artificial intelligence. It delves deeply into advanced topics like model building and deployment on AWS, setting it apart from broader-focused exams that cover general cloud infrastructure and solutions.

The AWS MLS-C01 exam comprises multiple-choice and multiple-response questions. It tests theoretical knowledge and practical application in machine learning, requiring a blend of understanding AWS services and implementing ML solutions on the AWS platform.

The AWS MLS-C01 exam duration is 180 minutes. Within this time, candidates must answer a comprehensive set of questions designed to assess their proficiency in AWS machine learning services and best practices in a time-managed environment.

The passing score for the AWS MLS-C01 exam is not fixed; it uses a scaled scoring method. Typically, scores range from 100 to 1000, with AWS setting the minimum passing score based on statistical analysis and exam difficulty.

Professionals targeting roles like Machine Learning Engineer, Data Scientist, AI Developer, and Cloud Solutions Architect benefit from MLS-C01 certification. It's valuable for those implementing ML/AI solutions on AWS, enhancing career prospects in data-driven and cloud computing fields.

Yes, you can retake the AWS MLS-C01 exam if you fail on your first attempt. However, there is a mandatory 14-day waiting period before you can retake the exam, and additional fees apply for each subsequent attempt.

The AWS MLS-C01 exam specifically focuses on machine learning and data science principles, including model training, deployment, and optimization. Unlike other AWS exams, it emphasizes practical knowledge of machine learning services, algorithms, and frameworks within the AWS ecosystem.

With an AWS MLS-C01 certification, you can pursue roles such as Machine Learning Engineer, Data Scientist, AI Developer, Machine Learning Architect, and Data Engineer. These positions involve developing, deploying, and optimizing machine learning models and solutions on the AWS platform.

The duration of the AWS MLS-C01 exam is 180 minutes. This three-hour period allows candidates to thoroughly address questions on machine learning concepts, algorithms, and AWS-specific services, ensuring a comprehensive evaluation of their knowledge and skills in the field.

AWS MLS-C01 certification can significantly enhance salary prospects in the tech industry by validating expertise in machine learning on AWS. Certified professionals often command higher salaries due to their specialized skills in developing and deploying advanced ML solutions on the cloud.

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AWS Certified Machine Learning - Specialty Questions and Answers

Questions 1

A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social media. The company needs to be able toevaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds.

The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages. The company already has a solution in place to collect posts and store them within an Amazon S3 bucket.

What services should the data science team use to deliver this solution?

Options:

A.

Train a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when posts are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table and in a custom Amazon CloudWatch metric. Use CloudWatch alarms to notify analysts of trends.

B.

Train a model in Amazon SageMaker by using the semantic segmentation algorithm to model the semantic content in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when objects are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notificati

C.

Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.

D.

Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in a custom Amazon CloudWatch metric and in S3. Use CloudWatch alarms to notify analysts of trends.

Questions 2

A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data

Which of the following services can feed data to the MapReduce jobs? (Select TWO )

Options:

A.

AWSDMS

B.

Amazon Kinesis

C.

AWS Data Pipeline

D.

Amazon Athena

E.

Amazon ES

Questions 3

A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training

What should the Specialist do to optimize the data for training on SageMaker'?

Options:

A.

Use the SageMaker batch transform feature to transform the training data into a DataFrame

B.

Use AWS Glue to compress the data into the Apache Parquet format

C.

Transform the dataset into the Recordio protobuf format

D.

Use the SageMaker hyperparameter optimization feature to automatically optimize the data

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