MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A small team runs a SageMaker real-time endpoint in production. They want a low-effort way to know when the endpoint's invocations are failing so they can react quickly, and they want the alert delivered to their on-call channel. Which approach requires the least custom code?
⚠ Common exam trap
The trap here is reaching for CloudTrail or Model Monitor to detect invocation failures, when the endpoint's built-in CloudWatch error metrics already expose them directly.
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
✓
Create an Amazon CloudWatch alarm on the endpoint's ModelLatency and Invocation4XXErrors metrics, and route the alarm to Amazon SNS with an email or chat subscription.
SageMaker endpoints emit CloudWatch metrics automatically, including invocation counts and 4XX and 5XX error metrics, so a CloudWatch alarm wired to an SNS topic gives failure notification without any custom instrumentation. Model Monitor and log-parsing pipelines solve different problems and require substantially more configuration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS CloudTrail to capture InvokeEndpoint API calls and create an Amazon EventBridge rule that notifies the on-call channel when errors occur.
Why it's wrong here
CloudTrail records the control-plane and data-plane API call itself, but a failed model invocation is typically a successful InvokeEndpoint call that returns an error status, so CloudTrail does not surface it as a distinct failure event. EventBridge rules on CloudTrail therefore miss the failures the team actually cares about.
- ✓
Create an Amazon CloudWatch alarm on the endpoint's ModelLatency and Invocation4XXErrors metrics, and route the alarm to Amazon SNS with an email or chat subscription.
Why this is correct
SageMaker automatically publishes endpoint metrics such as Invocations, Invocation4XXErrors, Invocation5XXErrors, and ModelLatency to CloudWatch without any instrumentation. Alarms on those metrics publish to an SNS topic that the on-call channel subscribes to, giving failure notification with essentially no custom code or additional services.
- ✗
Enable SageMaker Model Monitor on the endpoint and configure the monitor to raise an alarm when constraint violations are detected.
Why it's wrong here
Model Monitor evaluates data and model quality against baselines on a schedule, which is aimed at drift and quality regressions rather than invocation failures. It requires a baseline, captured data, and a monitoring schedule, so it is far more setup than needed for simple error alerting and does not directly surface 4XX or 5XX invocation errors.
- ✗
Subscribe an AWS Lambda function to the endpoint's CloudWatch log group and have the function parse logs and publish to Amazon SNS when errors appear.
Why it's wrong here
Parsing CloudWatch Logs with a Lambda function means writing and maintaining custom log-matching code, which contradicts the goal of minimizing effort. The built-in endpoint metrics already expose error counts, so this approach adds moving parts without providing information that alarms on the existing metrics would not already provide.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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