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 291–300 of 486

Q291

A company needs to secure its machine learning model endpoints. Which AWS feature should they enable?

  • A API Gateway
  • B CloudFront
  • C IAM Policies
  • D Amazon SNS
Explanation IAM Policies tightly control access to resources, while API Gateway is for endpoint management, CloudFront is for content delivery, and SNS handles notifications.
Q292

Which service provides automated model training and tuning?

  • A Amazon SageMaker
  • B AWS Glue
  • C Amazon QuickSight
  • D AWS Lambda
Explanation Amazon SageMaker is designed for building, training, and deploying machine learning models, while the others serve different purposes.
Q293

A company needs to deploy a machine learning model with low latency. What should they focus on?

  • A Batch processing
  • B Edge computing solutions
  • C Data archiving
  • D Sequential data storage
Explanation Edge computing solutions enhance low-latency performance by processing data near its source, unlike options A, C, and D.
Q294

What happens when an AWS Lambda function exceeds its timeout limit?

  • A It continues running until finished.
  • B It stops and returns an error.
  • C It resumes on the next trigger.
  • D Its execution is throttled.
Explanation Once the timeout limit is reached, the Lambda function stops executing and an error is returned, while the other options incorrectly describe its behavior.
Q295

What service automates machine learning model deployment?

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon EC2
  • D Amazon RDS
Explanation Amazon SageMaker provides built-in features for automated deployment and scaling, while others are not specifically designed for ML.
Q296

A company needs real-time predictions for incoming data streams. Which AWS service is ideal?

  • A AWS Batch
  • B Amazon Kinesis
  • C AWS Glue
  • D Amazon EMR
Explanation Amazon Kinesis is designed for processing real-time data streams, unlike the others which are not optimized for this use case.
Q297

What happens when an ML model has high bias?

  • A Underfits the training data
  • B Overfits the training data
  • C Performs well on new data
  • D Improves with more features
Explanation High bias indicates the model is too simplistic and fails to capture the underlying patterns, leading to underfitting.
Q298

A company needs to securely share data with multiple teams in AWS. Which AWS service should they use?

  • A AWS Lake Formation
  • B Amazon S3
  • C Amazon RDS
  • D AWS Glue
Explanation AWS Lake Formation simplifies securing and sharing data; S3 is for storage, RDS is for databases, and Glue is for ETL processes.
Q299

What happens when you deploy a non-compliant machine learning model on AWS?

  • A It will automatically fix itself.
  • B It won't deploy at all.
  • C It may produce inaccurate results.
  • D It will be monitored continuously.
Explanation A non-compliant model can still be deployed but may yield inaccurate or unreliable predictions; it won't fix automatically or be prevented from deploying.
Q300

You are configuring an AWS SageMaker training job. Which parameter is crucial for specifying the algorithm used?

  • A Training Job Name
  • B Input Data URI
  • C Algorithm Specification
  • D Instance Type
Explanation The Algorithm Specification parameter defines the specific algorithm to use, whereas the other options configure job attributes or resources.