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 341–350 of 486

Q341

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

  • A Amazon Kinesis
  • B Amazon RDS
  • C AWS Glue
  • D Amazon S3
Explanation Amazon Kinesis is specifically designed for real-time data streaming, unlike the other options.
Q342

What happens when you increase the training dataset size in an ML model?

  • A Model accuracy generally improves
  • B Training speed increases significantly
  • C Overfitting always decreases
  • D Data storage costs go up immediately
Explanation Increasing the dataset size typically leads to better model accuracy, while other options are incorrect effects.
Q343

You are configuring an AWS Lambda function to respond to Amazon S3 events. Which S3 bucket setting is vital for triggering the Lambda function?

  • A Bucket Versioning
  • B Bucket Policy
  • C Event Notifications
  • D Lifecycle Rules
Explanation Event Notifications must be set for Lambda triggers, while other options do not trigger events.
Q344

Which AWS service allows you to create predictive models based on structured data?

  • A Amazon Redshift
  • B Amazon SageMaker
  • C Amazon EMR
  • D AWS Glue
Explanation Amazon SageMaker is designed specifically for building predictive models, unlike the others.
Q345

A company needs to implement a solution for real-time anomaly detection in IoT sensor data. What is the best choice?

  • A Amazon EC2 with OpenCV
  • B Amazon Kinesis Data Analytics
  • C AWS Batch processing
  • D Amazon RDS with triggers
Explanation Amazon Kinesis Data Analytics processes streaming data in real-time, unlike the other options.
Q346

Which service provides batch processing for machine learning jobs?

  • A AWS Batch
  • B AWS Lambda
  • C AWS Glue
  • D Amazon SageMaker
Explanation AWS Batch efficiently manages batch processing jobs, while the others are for real-time processing or data integration tasks.
Q347

A company needs to expose a machine learning model via an API. What should they use?

  • A Amazon SageMaker Endpoint
  • B AWS Step Functions
  • C Amazon S3
  • D AWS CloudFormation
Explanation Amazon SageMaker Endpoints are specifically designed for deploying models as APIs, unlike the other options.
Q348

What happens when you reduce the number of training epochs in a deep learning model?

  • A Underfitting may occur
  • B Overfitting will increase
  • C Model performance becomes optimal
  • D Training time doubles
Explanation Reducing epochs can lead to underfitting since the model may not learn enough from the data, while overfitting typically results from too many epochs.
Q349

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

  • A Amazon SageMaker
  • B Amazon S3
  • C AWS Lambda
  • D Amazon EC2
Explanation Amazon SageMaker is purpose-built for model deployment, while others lack specific ML deployment features.
Q350

A company needs to train a model on sensitive data. What is the best practice?

  • A Use public data without encryption
  • B Train on-site without firewalls
  • C Leverage AWS SageMaker with encryption
  • D Share data with third-party vendors
Explanation Using AWS SageMaker with encryption ensures data security, while the other options compromise data safety.