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 401–410 of 486

Q401

A company needs real-time predictions for user queries. Which AWS service should they use?

  • A Amazon S3
  • B Amazon Kinesis
  • C AWS Batch
  • D Amazon Redshift
Explanation Amazon Kinesis is designed for real-time data processing while the others are more suited for batch or storage.
Q402

What happens when you run a model without feature normalization?

  • A Model converges faster
  • B Accuracy may decrease
  • C Training time decreases
  • D Features become irrelevant
Explanation Lack of normalization can cause features with larger ranges to dominate, decreasing model accuracy.
Q403

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

  • A Amazon Kinesis
  • B Amazon S3
  • C Amazon RDS
  • D Amazon DynamoDB
Explanation Amazon Kinesis is designed specifically for real-time streaming, while the others serve different purposes like storage and databases.
Q404

A company needs to host a machine learning model that can scale automatically. Which service should they choose?

  • A AWS Lambda
  • B Amazon SageMaker
  • C EC2 Spot Instances
  • D Amazon RDS
Explanation Amazon SageMaker automatically manages scaling for deployed models, while the others do not focus specifically on ML model hosting.
Q405

What happens when you delete an S3 bucket that is not empty?

  • A You must empty it first
  • B All objects are deleted permanently
  • C You receive a warning only
  • D ARestoration tool is activated
Explanation You must empty the bucket before deletion, otherwise the operation fails, and the other options are incorrect based on S3's behavior.
Q406

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

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon EC2
  • D Amazon RDS
Explanation Amazon SageMaker is specifically designed for building, training, and deploying ML models effectively, while others are for compute, database, or serverless computing.
Q407

A company needs to monitor the accuracy of its model predictions in real-time. What AWS service should they use?

  • A Amazon CloudWatch
  • B AWS Glue
  • C Amazon S3
  • D AWS Lambda
Explanation Amazon CloudWatch is used for monitoring and logging metrics in real-time, while the other services focus on data integration or storage.
Q408

You are configuring a model to minimize bias during training. Which technique can help achieve this?

  • A Data Augmentation
  • B Overfitting
  • C Training with fewer features
  • D Increased batch size
Explanation Data Augmentation can help create a more balanced dataset, while the other options may lead to overfitting or other issues.
Q409

Which Amazon service is used for real-time stream processing?

  • A Amazon Kinesis
  • B Amazon S3
  • C Amazon EC2
  • D Amazon RDS
Explanation Amazon Kinesis is specifically designed for processing streaming data in real-time, unlike the others that serve different purposes.
Q410

A company needs to deploy a scalable machine learning model on a serverless architecture. Which solution should they choose?

  • A Amazon SageMaker
  • B AWS Lambda
  • C Amazon EC2
  • D AWS Batch
Explanation Amazon SageMaker provides built-in support for deploying models in serverless environments, while the others require more management.