A team deploys a churn prediction model to a SageMaker endpoint. The model was trained on data from the past year. After deployment, the team notices that the model's prediction accuracy is declining. They suspect the data distribution has changed. The team wants to set up monitoring to detect this and automatically retrain the model when drift is detected. What should the team do?

Use AWS CloudTrail to monitor API calls and detect anomalies.

Use CloudWatch alarms on the endpoint's CPU utilization to detect changes in traffic.

Use SageMaker Debugger to analyze training jobs and detect overfitting.

Use SageMaker Model Monitor to detect data drift and create a CloudWatch alarm that triggers a SageMaker Pipeline to retrain the model.

AWSCertified Machine Learning Engineer - Associate

Domain 4Objective 2

Task 4.2: Monitor and Optimize Infrastructure and Costs MLA-C01 Practice Questions (Page 4)

Part of the Content Domain 4: ML Solution Monitoring, Maintenance, and Security domain, which accounts for 24% of the MLA-C01 exam. AWS does not publish an official question count, but from its 130-minute exam (~50–85 total, ~12–20 in this domain), expect 4–7 from this objective — we provide 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

8concepts

24%of the exam

Questions 16–20

16foundation · easy
Which strategy adjusts the number of EC2 instances in an ML inference fleet based on demand to avoid over-provisioning and reduce costs?
Select an answer first
17foundation · easy
Which practice helps reduce training job costs by selecting the most appropriate instance type that meets the job's memory and compute requirements without over-provisioning?
Select an answer first
18foundation · easy
Which compute option is serverless and can be used for lightweight ML inference or preprocessing tasks, where you only pay for the compute time consumed during execution?
Select an answer first
19expert · hard
A team deploys a churn prediction model to a SageMaker endpoint. The model was trained on data from the past year. After deployment, the team notices that the model's prediction accuracy is declining. They suspect the data distribution has changed. The team wants to set up monitoring to detect this and automatically retrain the model when drift is detected. What should the team do?
Select an answer first
20foundation · easy
Which AWS service is a fully managed platform that provides built-in ML algorithms, automatic model tuning, and managed training and inference infrastructure, reducing the operational overhead of running ML workloads?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to MLA-C01

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by AWS. “MLA-C01” is a trademark of its owner, used for identification only.

Task 4.2: Monitor and Optimize Infrastructure and Costs MLA-C01 Practice Questions (Page 4)

Questions 16–20

Which strategy adjusts the number of EC2 instances in an ML inference fleet based on demand to avoid over-provisioning and reduce costs?

Which practice helps reduce training job costs by selecting the most appropriate instance type that meets the job's memory and compute requirements without over-provisioning?

Which compute option is serverless and can be used for lightweight ML inference or preprocessing tasks, where you only pay for the compute time consumed during execution?

Which AWS service is a fully managed platform that provides built-in ML algorithms, automatic model tuning, and managed training and inference infrastructure, reducing the operational overhead of running ML workloads?

Keep studying — Content Domain 4: ML Solution Monitoring, Maintenance, and Security