AWS Certified Machine Learning Engineer - Associate MLA-C01 Practice Test

AWS Certified Machine Learning Engineer - Associate MLA-C01 Practice Test practice questions, page 5 of 11, with answer review and explanations.

Question 21

A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months. Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

  1. Spot Instances
  2. Reserved Instances
  3. On-Demand Instances
  4. Dedicated Instances

Question 22

An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account В in the same Region. Which solution will meet this requirement with the LEAST development effort?

  1. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
  2. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
  3. Use AWS DataSync to replicate the model from Account A to Account B.
  4. Create an AWS Site-to-Site VPN connection between Account A and Account В to transfer the model.

Question 23

An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs. Which solutions will mitigate this problem? (Choose two.)

  1. Enable early stopping on the model.
  2. Increase dropout in the layers.
  3. Increase the number of layers.
  4. Increase the number of neurons.
  5. Investigate and reduce the sources of model bias.

Question 24

A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket. Which solution will meet these requirements?

  1. Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.
  2. Use a custom Amazon SageMaker notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.
  3. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.
  4. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.

Question 25

A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict. The company needs to use the dataset in a solution to determine if a model can predict the target variable. Which solution will provide this information with the LEAST development effort?

  1. Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
  2. Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
  3. Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
  4. Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.