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Amazon Web Services MLS-C01 Dumps

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AWS Certified Machine Learning - Specialty
Last Update: Apr 16, 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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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.

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AWS Certified Machine Learning - Specialty Questions and Answers

Questions 1

The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?



The model predicts both the trend and the seasonality well.


The model predicts the trend well, but not the seasonality.


The model predicts the seasonality well, but not the trend.


The model does not predict the trend or the seasonality well.

Questions 2

A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.

Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)



AWS CloudTrail


AWS Health


AWS Trusted Advisor


Amazon CloudWatch


AWS Config

Questions 3

A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.

Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?



Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.


Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.


Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.


Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.

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