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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 16–18 of 24 questions

Question 16 (Topic 1)

An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions. When the model is deployed, the model needs to access large amounts of data to process requests. The requests can involve as much as 100 MB of data. Which deployment solution will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

  • ○ Deploy the model to Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer.
  • ○ Deploy the model to an Amazon SageMaker real-time endpoint.
  • ○ Deploy the model to an Amazon SageMaker Asynchronous Inference endpoint.
  • ○ Package the model as a container. Deploy the model to Amazon Elastic Container Service (Amazon ECS) on Amazon EC2 instances.
Question 17 (Topic 1)

An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either “yes” or “no.” The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset. Which methods will meet these requirements? (Choose two.)

Select an option, then click Submit answer.

  • ○ Binary encoding
  • ○ Label encoding
  • ○ One-hot encoding
  • ○ Statistical imputation
  • ○ Tokenization
Question 18 (Topic 1)

A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket. Data transformation scripts and queries are now taking much longer than they used to take. An ML engineer must implement a solution to optimize the data for query performance. Which solution will meet this requirement with the LEAST operational overhead?

Select an option, then click Submit answer.

  • ○ Configure an AWS Lambda function to split the .csv files into smaller objects in the S3 bucket.
  • ○ Configure an AWS Glue job to drop columns that have string type values and to save the results to the S3 bucket.
  • ○ Configure an AWS Glue extract, transform, and load (ETL) job to convert the .csv files to Apache Parquet format.
  • ○ Configure an Amazon EMR cluster to process the data that is in the S3 bucket.