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 471–480 of 486

Q471

You are configuring a model that predicts whether customers will buy a product. What can you do to avoid overfitting?

  • A Reduce model complexity
  • B Increase training data size
  • C Use cross-validation techniques
  • D All of the above
Explanation All options are valid strategies to prevent overfitting; each contributes effectively to model generalization.
Q472

Which service is best for real-time data processing?

  • A Amazon Kinesis
  • B Amazon S3
  • C Amazon RDS
  • D Amazon EC2
Explanation Amazon Kinesis is designed for real-time data processing, while the others are not focused on this use case.
Q473

A company needs to enforce strict access control on its S3 bucket. What should they use?

  • A Bucket Policies
  • B S3 Lifecycle Policies
  • C S3 Versioning
  • D S3 Transfer Acceleration
Explanation Bucket Policies allow fine-grained access control, while the other options do not handle access restrictions.
Q474

You are configuring a SageMaker training job. What happens if you request more resources than available?

  • A Job fails instantly
  • B Job waits until resources are free
  • C Job allocates partial resources
  • D Job uses local resources only
Explanation The job fails instantly when requested resources exceed the available capacity, as it cannot proceed without sufficient resources.
Q475

Which service is primarily used for building ML models in AWS?

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon S3
  • D AWS Elastic Beanstalk
Explanation Amazon SageMaker is designed for ML model development, while the others serve different purposes.
Q476

A company needs to automate their data labeling process. Which service can they use?

  • A Amazon Rekognition
  • B Amazon SageMaker Ground Truth
  • C AWS Glue
  • D Amazon Comprehend
Explanation Amazon SageMaker Ground Truth specifically helps with data labeling, while the others focus on different tasks.
Q477

You are configuring an ML model for real-time predictions. What parameter is crucial to optimize?

  • A Latency
  • B Storage Size
  • C Training Time
  • D Data Volume
Explanation Latency is critical for real-time predictions, while others concern different aspects.
Q478

Which AWS service is best for serving machine learning models at scale?

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon EC2
  • D Amazon S3
Explanation Amazon SageMaker is specifically designed for building, training, and deploying ML models at scale, while other options serve different functions.
Q479

A company needs to automate model deployment after training. Which AWS feature is most suitable?

  • A AWS Step Functions
  • B AWS Glue
  • C Amazon EMR
  • D Amazon FSx
Explanation AWS Step Functions allows for orchestration and automation of model deployment processes effectively.
Q480

You are configuring an S3 bucket for machine learning dataset storage with GPU access. What AWS feature should you enable?

  • A S3 Lifecycle Policies
  • B S3 Event Notifications
  • C S3 Transfer Acceleration
  • D S3 Object Lock
Explanation S3 Transfer Acceleration improves upload speeds for large datasets, important for efficient ML workflows.