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MLA-C01 Exam Dumps : AWS Certified Machine Learning Engineer - Associate

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AWS Certified Machine Learning Engineer - Associate Questions and Answers

Question 1

An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.

Which solution will meet these requirements?

Options:

A.

Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.

B.

Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.

C.

Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.

D.

Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.

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Question 2

A company uses AWS CodePipeline to orchestrate a continuous integration and continuous delivery (CI/CD) pipeline for ML models and applications.

Select and order the steps from the following list to describe a CI/CD process for a successful deployment. Select each step one time. (Select and order FIVE.)

. CodePipeline deploys ML models and applications to production.

· CodePipeline detects code changes and starts to build automatically.

. Human approval is provided after testing is successful.

. The company builds and deploys ML models and applications to staging servers for testing.

. The company commits code changes or new training datasets to a Git repository.

Options:

Question 3

A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.

Which solution will meet this requirement?

Options:

A.

Configure the competitor's name as a blocked phrase in Amazon Q Business.

B.

Configure an Amazon Q Business retriever to exclude the competitor’s name.

C.

Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.

D.

Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.