The answer is to set up the SageMaker Model Registry to trigger a Lambda function on approval that updates the endpoint using the new model version. This approach automates SageMaker model deployment by creating an event-driven pipeline: when a new model version is approved in the registry, an EventBridge rule invokes a Lambda function that programmatically updates the endpoint’s production variant with the latest model artifact. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of integrating Model Registry with serverless compute to close the loop between training and serving—a common trap is assuming a successful pipeline run automatically updates the endpoint, but without an explicit deployment trigger, the endpoint remains stale. Remember the key chain: Pipeline trains → Registry approves → EventBridge fires → Lambda deploys. For exam day, think “approval equals action”—if the registry approves but nothing triggers, the endpoint never refreshes.
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A retail company has deployed a real-time recommendation model on a SageMaker endpoint. The model is trained daily using SageMaker Pipelines that process user interaction data from a large S3 bucket. Recently, the operations team noticed that the endpoint's predictions have become stale; users are seeing recommendations based on data from days ago. The pipeline runs successfully every day at 2 AM UTC, but the endpoint continues to serve the old model version. The team checks the pipeline and finds no errors. The model registry contains multiple model versions approved automatically. The endpoint is configured with production variants, but only one variant is active. The team suspects the issue is with the deployment step in the pipeline. They want to automatically deploy new model versions to the endpoint as soon as they are registered and approved. What should they do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set up the SageMaker Model Registry to trigger a Lambda function on approval that updates the endpoint using the new model version.
Option B is correct because using a SageMaker Model Registry with an automatic deployment pipeline (via EventBridge or Lambda triggered by approval) ensures new models are deployed when approved. Option A (manual approval) is not automatic. Option C (test on batch transform) doesn't deploy to endpoint. Option D (change endpoint configuration manually) is not automated and suggested they do currently? Actually they need automation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Set up the SageMaker Model Registry to trigger a Lambda function on approval that updates the endpoint using the new model version.
Why this is correct
Event-driven deployment ensures immediate update on approval.
Related concept
Read the scenario before looking for a memorised answer.
✗
Configure the pipeline to stop automatic approval and require manual approval before deployment.
Why it's wrong here
Manual approval would add delay, not solve the stale model issue.
✗
Modify the pipeline to run batch transforms on the new model and compare metrics, then update the endpoint.
Why it's wrong here
Batch transform is for offline inference, not for real-time endpoint update.
✗
Create a daily cron job that checks for new model versions and manually updates the endpoint configuration.
Why it's wrong here
Cron job introduces delay and is not event-driven.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set up the SageMaker Model Registry to trigger a Lambda function on approval that updates the endpoint using the new model version. — Option B is correct because using a SageMaker Model Registry with an automatic deployment pipeline (via EventBridge or Lambda triggered by approval) ensures new models are deployed when approved. Option A (manual approval) is not automatic. Option C (test on batch transform) doesn't deploy to endpoint. Option D (change endpoint configuration manually) is not automated and suggested they do currently? Actually they need automation.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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