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 1–10 of 486

Q1

Which service is best for real-time data analytics on streaming data?

  • A Amazon Kinesis
  • B Amazon S3
  • C Amazon RDS
  • D AWS Lambda
Explanation Amazon Kinesis is designed for real-time data processing, while S3 is for storage, RDS for databases, and Lambda for serverless compute tasks.
Q2

A company needs to securely manage API access. Which AWS service should they use?

  • A Amazon EC2
  • B AWS Key Management Service
  • C AWS Identity and Access Management
  • D Amazon Aurora
Explanation AWS IAM is specifically designed for managing access to AWS resources, whereas other options do not serve this purpose.
Q3

What happens when an AWS Lambda function runs longer than its configured timeout?

  • A Function continues executing without interruption
  • B Function is terminated without returning response
  • C Function retries from the beginning
  • D Function executes with reduced performance
Explanation When the configured timeout is reached, AWS Lambda terminates the function and does not return a response, ensuring efficient resource management.
Q4

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

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon S3
  • D AWS EC2
Explanation Amazon SageMaker is specifically designed for deploying ML models, while the others are not.
Q5

A company needs to process streaming data in real time. Which service should they use?

  • A Amazon Rekognition
  • B Amazon Kinesis
  • C AWS Glue
  • D Amazon Redshift
Explanation Amazon Kinesis is designed for real-time data processing while the others handle different types of data or analytics.
Q6

What happens when you remove a feature from a model during training?

  • A Model accuracy always improves
  • B Model performance may decrease
  • C Model becomes simpler in structure
  • D Training time increases
Explanation Removing features can lead to decreased model performance if those features were informative.
Q7

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

  • A Amazon SageMaker
  • B Amazon Glacier
  • C Amazon RDS
  • D AWS Lambda
Explanation Amazon SageMaker is designed for ML model building, while others serve different purposes.
Q8

A company needs to store a large volume of unstructured data for analytics. Which AWS service should they use?

  • A Amazon Aurora
  • B Amazon DynamoDB
  • C Amazon S3
  • D Amazon Redshift
Explanation Amazon S3 is optimized for storing and retrieving large amounts of unstructured data compared to other options.
Q9

What happens when you deploy a model in Amazon SageMaker using the Multi-Model Endpoint?

  • A Supports only one model per endpoint
  • B Loads multiple models on demand
  • C Increases latency for all requests
  • D Requires dedicated infrastructure setup
Explanation Multi-Model Endpoints allow on-demand loading of multiple models for efficient resource usage, unlike the other options.
Q10

Which service is optimal for real-time inference?

  • A Amazon SageMaker Endpoint
  • B AWS Glue
  • C Amazon S3
  • D AWS Batch
Explanation Amazon SageMaker Endpoint provides low-latency inference, whereas the others are not designed for real-time predictions.