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Free AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 – Amazon AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Practice Test Question

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Showing 22–24 of 24 questions

Question 22 (Topic 1)

An ML engineer is building a logistic regression model to predict customer churn for subscription services. The ML engineer is using a dataset that contains two string variables: location and job_seniority_level. The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values. The ML engineer must perform preprocessing on the variables. Which solution will meet this requirement?

Select an option, then click Submit answer.

  • ○ Apply tokenization to location. Apply ordinal encoding to job_seniority_level.
  • ○ Apply one-hot encoding to location. Apply ordinal encoding to job_seniority_level
  • ○ Apply binning to location. Apply standard scaling to job_seniority_level.
  • ○ Apply one-hot encoding to location. Apply standard scaling to job_seniority_level.
Question 23 (Topic 1)

An ML engineer needs to develop an AI assistant by using Retrieval Augmented Generation (RAG) in Amazon Bedrock. The company stores a collection of PDF text files in an Amazon S3 bucket. The ML engineer must create an Amazon Bedrock knowledge base to process the PDF files and to store the processed files in a vector store. Which solution will meet these requirements?

Select an option, then click Submit answer.

  • ○ Use the latest version of Amazon Titan Text Premier to perform document embedding.
  • ○ Use the latest version of Mistral 7B Instruct to perform instruction tuning.
  • ○ Use the latest version of Anthropic Claude Sonnet to perform instruction tuning.
  • ○ Use Cohere Embed Multilingual to perform document embedding.
Question 24 (Topic 1)

A digital media entertainment company needs real-time video content moderation to ensure compliance during live streaming events. Which solution will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

  • ○ Use Amazon Rekognition and AWS Lambda to extract and analyze the metadata from the videos’ image frames.
  • ○ Use Amazon Rekognition and a large language model (LLM) hosted on Amazon Bedrock to extract and analyze the metadata from the videos’ image frames.
  • ○ Use Amazon SageMaker AI to extract and analyze the metadata from the videos’ image frames.
  • ○ Use Amazon Transcribe and Amazon Comprehend to extract and analyze the metadata from the videos’ image frames.