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 111–120 of 486

Q111

What happens when you deploy a model to AWS SageMaker with an incorrect input data schema?

  • A Model will train correctly
  • B Prediction will not be returned
  • C Model will ignore the incorrect schema
  • D Deployment will fail
Explanation Incorrect input data schema prevents predictions from being returned, while other options misrepresent the deployment behavior.
Q112

A company needs a way to improve its model performance while avoiding overfitting. Which technique should they use?

  • A Cross-validation
  • B Data augmentation
  • C Feature scaling
  • D Increasing training data size
Explanation Cross-validation helps assess model performance on different subsets, catching overfitting early. The other options improve training but won't directly prevent overfitting.
Q113

You are configuring a Batch Inference job in Amazon SageMaker. What happens when you select 'Asynchronous'?

  • A Inference happens instantly.
  • B Job runs and completes in the foreground.
  • C Remote service waits for your input.
  • D Job runs independently and returns an output later.
Explanation Asynchronous jobs run independently, allowing you to continue working. The other options suggest immediate feedback or synchronous processing.
Q114

Which AWS service should be used to deploy machine learning models as REST APIs?

  • A Amazon S3
  • B AWS Lambda
  • C Amazon SageMaker
  • D Amazon EC2
Explanation Amazon SageMaker simplifies deploying ML models as REST APIs. The other services can host applications but aren't specifically designed for machine learning inference in this context.
Q115

Which service is best suited for model training on large datasets in AWS?

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon EC2
  • D Amazon RDS
Explanation Amazon SageMaker is designed for model training, while the others are not specialized for this purpose.
Q116

A company needs to make predictions in real-time using a machine learning model. What AWS service should they choose?

  • A Amazon Redshift
  • B AWS Batch
  • C Amazon SageMaker Endpoint
  • D AWS Glue
Explanation Amazon SageMaker Endpoint offers real-time predictions, while the others focus on batch processing or ETL.
Q117

You are configuring an AWS IAM policy to restrict access to S3 buckets. What happens if you set a condition that denies access based on the IP address?

  • A Access is always denied.
  • B Access is denied only from specified IPs.
  • C Access is granted to specified IPs.
  • D Access is fully unrestricted.
Explanation Access is denied to any requests from the specified IPs, while allowing otherwise.
Q118

Which service provides managed machine learning model deployment?

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon EC2
  • D AWS Batch
Explanation Amazon SageMaker is specifically designed for model deployment, while the others serve different functions.
Q119

A company needs real-time data processing for streaming applications. Which service should they choose?

  • A Amazon S3
  • B Amazon Kinesis
  • C AWS Snowball
  • D Amazon RDS
Explanation Amazon Kinesis is tailored for real-time processing, unlike the other options.
Q120

What happens when a model in Amazon SageMaker fails to train properly?

  • A Training is retried automatically
  • B An error report is generated
  • C Model outputs previous predictions
  • D Training is paused indefinitely
Explanation An error report is generated to help identify the issue, while the other options are incorrect outcomes.