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Score Higher on Verified MLA-C01 | AWS Certified Machine Learning Engineer - Associate Exam Questions with Answers

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  • Exam Name: AWS Certified Machine Learning Engineer - Associate
  • Last Update: 01-Mar-2026
  • Questions and Answers: 207
  • Single Choice: 188 Q&A's
  • Multiple Choice: 7 Q&A's
  • Hotspot: 12 Q&A's

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MLA-C01 Questions and Answers

Question # 1

A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.

Which solution will meet these requirements with the LEAST effort?

A.

Use SageMaker built-in algorithms to train the proprietary datasets.

B.

Use SageMaker script mode and premade images for ML frameworks.

C.

Build a container on AWS that includes custom packages and a choice of ML frameworks.

D.

Purchase similar production models through AWS Marketplace.

Question # 2

A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.

Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

A.

Spot Instances

B.

Reserved Instances

C.

On-Demand Instances

D.

Dedicated Instances

Question # 3

A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.

An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.

Which solution will meet these requirements?

A.

Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.

B.

Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.

C.

Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

D.

Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

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