- A
Manually update the endpoint to point to the previous model version
Why wrong: Manual rollback is not automatic and may take too long.
- B
Configure the SageMaker endpoint deployment with traffic shifting and set up CloudWatch alarms to trigger automatic rollback
SageMaker supports canary or linear traffic shifting with automatic rollback based on CloudWatch alarms.
- C
Create multiple endpoints and use Amazon Route 53 weighted routing to shift traffic
Why wrong: This adds complexity and does not natively support automatic rollback.
- D
Use AWS CodeDeploy with Amazon EC2 instances behind an Elastic Load Balancer
Why wrong: CodeDeploy is for EC2/on-premises, not SageMaker endpoints.
Question 323 of 1,000
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 company deploys a machine learning model as a SageMaker real-time endpoint. They need to implement a mechanism to automatically roll back to the previous model version if performance degrades after a deployment. Which approach should they use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure the SageMaker endpoint deployment with traffic shifting and set up CloudWatch alarms to trigger automatic rollback
Option B is correct because SageMaker endpoints support deployment with traffic shifting (e.g., canary or linear patterns) via the 'DeploymentConfig' parameter, and you can attach CloudWatch alarms to the endpoint's variant metrics. If the alarm triggers (e.g., due to increased error rate or latency), SageMaker automatically rolls back the traffic to the previous model version, ensuring minimal manual intervention and fast recovery.
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.
- ✗
Manually update the endpoint to point to the previous model version
Why it's wrong here
Manual rollback is not automatic and may take too long.
- ✓
Configure the SageMaker endpoint deployment with traffic shifting and set up CloudWatch alarms to trigger automatic rollback
Why this is correct
SageMaker supports canary or linear traffic shifting with automatic rollback based on CloudWatch alarms.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create multiple endpoints and use Amazon Route 53 weighted routing to shift traffic
Why it's wrong here
This adds complexity and does not natively support automatic rollback.
- ✗
Use AWS CodeDeploy with Amazon EC2 instances behind an Elastic Load Balancer
Why it's wrong here
CodeDeploy is for EC2/on-premises, not SageMaker endpoints.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse manual rollback (Option A) as acceptable automation, or they overcomplicate the solution with external services like Route 53 (Option C) or CodeDeploy (Option D), missing that SageMaker's native deployment configuration with CloudWatch alarms provides a fully automated, integrated rollback mechanism.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker's traffic shifting uses endpoint variants with weighted routing; you can specify a 'DeploymentConfig' with a 'BlueGreenUpdatePolicy' that includes 'TrafficRoutingConfiguration' (e.g., 'CanarySize' and 'LinearStepSize'). CloudWatch alarms monitor metrics like 'ModelLatency' or 'Invocation4XXErrors' per variant, and when an alarm enters 'ALARM' state, SageMaker automatically executes the rollback by shifting 100% of traffic back to the previous variant. A subtle behavior is that the rollback only reverts traffic, not the model artifacts—you must ensure the previous model version remains available in the endpoint's production variant.
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.
TExam Day Tips
- 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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: Configure the SageMaker endpoint deployment with traffic shifting and set up CloudWatch alarms to trigger automatic rollback — Option B is correct because SageMaker endpoints support deployment with traffic shifting (e.g., canary or linear patterns) via the 'DeploymentConfig' parameter, and you can attach CloudWatch alarms to the endpoint's variant metrics. If the alarm triggers (e.g., due to increased error rate or latency), SageMaker automatically rolls back the traffic to the previous model version, ensuring minimal manual intervention and fast recovery.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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