Amazon AWS

AWS Certified Machine Learning Engineer – Associate

MLA-C01

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam validates your skills in building, training, and deploying machine learning models on AWS. It is ideal for those looking to specialize in machine learning.

486 questions 0 views Free
Start Mock Test Timed · Full-length · Scored

Questions 71–80 of 486

Q71

A company needs to optimize its machine learning models based on feedback from A/B tests. Which AWS tool would best support this?

  • A AWS CodePipeline
  • B Amazon SageMaker
  • C AWS CloudFormation
  • D Amazon RDS
Explanation Amazon SageMaker includes features for A/B testing and model optimization, unlike the other tools listed.
Q72

You are configuring an S3 bucket for ML model storage. What happens if you disable public access settings?

  • A All files are deleted.
  • B Files become inaccessible for all.
  • C Public access is restricted.
  • D Performance is improved significantly.
Explanation Disabling public access restricts access only to unauthorized public users, not to authorized accounts.
Q73

Which AWS service is primarily used for building machine learning models?

  • A Amazon SageMaker
  • B Amazon Aurora
  • C Amazon CloudFront
  • D Amazon RDS
Explanation Amazon SageMaker specializes in building ML models; others serve different functions.
Q74

A company needs to automate its model deployment. Which AWS feature should they use?

  • A Data Pipeline
  • B SageMaker Model Registry
  • C CloudFormation
  • D IAM Roles
Explanation SageMaker Model Registry supports automated model deployment; others do not directly serve this purpose.
Q75

You are configuring an IAM role for a SageMaker training job. What must be included?

  • A S3 bucket policies
  • B CodePipeline permissions
  • C Logger access
  • D S3 access permissions
Explanation S3 access permissions are needed for data access; others are not relevant for SageMaker training.
Q76

Which service is best for managing machine learning models at scale?

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon S3
  • D AWS CloudFormation
Explanation Amazon SageMaker offers comprehensive tools for developing, training, and deploying ML models. AWS Lambda is for serverless compute, Amazon S3 is storage, and AWS CloudFormation is for infrastructure management.
Q77

A company needs to process large batches of images for classification. Which AWS service should be used?

  • A AWS Step Functions
  • B Amazon Textract
  • C Amazon Rekognition
  • D AWS Batch
Explanation AWS Batch efficiently handles batch processing of jobs, while the others serve different purposes like image recognition or document processing.
Q78

What happens when a training job in SageMaker fails due to insufficient instance resources?

  • A Job succeeds with limited resources
  • B Job is retried automatically
  • C Job fails and stops running
  • D Job runs with different parameters
Explanation A training job that fails due to resource limitations will terminates without automatic retries or adjustments. The other options do not represent the actual behavior of SageMaker when resource errors occur.
Q79

Which service is best for real-time event processing in AWS?

  • A AWS Lambda
  • B Amazon S3
  • C AWS CloudFormation
  • D Amazon Athena
Explanation AWS Lambda allows real-time event processing while S3 is for storage and others are for different purposes.
Q80

A company needs to preprocess large datasets and run batch processing jobs in AWS. Which service should they use?

  • A AWS Glue
  • B Amazon SageMaker
  • C Amazon RDS
  • D AWS Lambda
Explanation AWS Glue is designed for data preparation and ETL jobs, while others are not optimized for batch processing.