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 231–240 of 486

Q231

You are configuring a batch processing job. What should you ensure about your input data?

  • A Data must be encrypted only
  • B Data is partitioned correctly
  • C Data must be in JSON format
  • D Data must be saved in Glacier
Explanation Correct data partitioning is crucial for optimizing batch processing, while others are not universally required.
Q232

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

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon S3
  • D Amazon RDS
Explanation Amazon SageMaker simplifies model deployment, while the others serve different purposes.
Q233

A company needs to preprocess data for a training job without provisioning servers. What should they use?

  • A Amazon SageMaker Processing
  • B AWS Glue
  • C Amazon EC2
  • D AWS Data Pipeline
Explanation Amazon SageMaker Processing is serverless for data preprocessing; the other options require more management.
Q234

What happens when you deploy an ML model with an AWS Lambda function with insufficient memory allocated?

  • A Increased execution time
  • B Function execution fails
  • C Billed for zero memory
  • D AWS Service shuts down
Explanation Insufficient memory can cause function execution failure, while others do not typically occur.
Q235

Which AWS service is best for real-time data processing?

  • A Amazon Kinesis
  • B Amazon S3
  • C AWS Glue
  • D Amazon RDS
Explanation Amazon Kinesis is designed for real-time data streams, while the others are for storage or batch processing.
Q236

A company needs to scale its ML model based on varying demand. Which AWS service should they consider?

  • A AWS Step Functions
  • B AWS Batch
  • C Amazon SageMaker
  • D Amazon DynamoDB
Explanation Amazon SageMaker offers built-in scalability for ML model deployment compared to the other services listed.
Q237

What happens when an EC2 instance is stopped and started again?

  • A Same IP is retained
  • B Always new IP allocated
  • C Data on EBS is lost
  • D Instance type changes automatically
Explanation Stopping and starting an EC2 instance often causes it to receive a new public IP, while EBS data remains intact.
Q238

A company needs to perform real-time analytics on streaming data. Which AWS service should they use?

  • A Amazon Kinesis
  • B Amazon S3
  • C AWS Glue
  • D Amazon RDS
Explanation Amazon Kinesis is designed for real-time data processing, while the others are not suitable for streaming data.
Q239

You are training a model with AWS SageMaker using a large dataset stored in S3. What benefits do you gain from using SageMaker's built-in algorithms over your custom algorithm?

  • A Higher performance without optimization
  • B No requirement for ML expertise
  • C Managed infrastructure and scalability
  • D Unlimited data size allowance
Explanation SageMaker manages the infrastructure and scales automatically, unlike custom implementations.
Q240

What happens when you enable versioning on an S3 bucket?

  • A Older files will be deleted
  • B Files will be encrypted
  • C Multiple versions of objects are stored
  • D Access speed is increased
Explanation Enabling versioning allows multiple versions of the same object to be stored in the bucket, unlike the other options.