MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A financial services company must ensure that a SageMaker model deployed to a real-time endpoint only produces predictions consistent with a fairness constraint on a protected attribute, and that any violation is detected within minutes and triggers an alert to the compliance team. The model is already deployed and monitored for data quality. Which approach should the machine learning engineer implement?
⚠ Common exam trap
Test-takers frequently confuse Clarify explainability or data quality statistics with bias monitoring, when only a Clarify bias baseline evaluated by Model Monitor measures fairness constraints on a protected attribute over time.
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
✓
Enable SageMaker Model Monitor with a bias drift baseline created by SageMaker Clarify, schedule the monitoring job to run every few minutes, and configure CloudWatch alarms on the bias metrics.
Bias drift monitoring in Model Monitor compares live predictions against a Clarify-generated bias baseline that encodes the fairness constraint, computing metrics on captured data. Running the job frequently yields detection within minutes, and publishing those metrics to CloudWatch enables alarms that notify compliance. Explainability, data quality, and registry gating do not evaluate outcome fairness on a protected attribute.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable SageMaker Model Monitor with a bias drift baseline created by SageMaker Clarify, schedule the monitoring job to run every few minutes, and configure CloudWatch alarms on the bias metrics.
Why this is correct
Model Monitor supports bias drift monitoring using a Clarify-generated baseline, which defines the fairness constraint and computes bias metrics on captured data. Scheduling the job at a short interval detects violations within minutes, and emitting the metrics to CloudWatch lets alarms notify the compliance team. This directly ties the fairness constraint to automated detection and alerting.
- ✗
Use SageMaker Model Registry to gate the model on a fairness condition and configure EventBridge to notify the compliance team when the model version changes.
Why it's wrong here
Model Registry governs approval and promotion of model versions; it does not continuously evaluate a deployed model's predictions against a fairness constraint. EventBridge notifications fire on registry events such as version changes, not on runtime bias violations. Since the model is already deployed and monitoring must be continuous, this approach cannot detect a live fairness breach within minutes.
- ✗
Attach a Clarify explainability job to the endpoint and configure a CloudWatch alarm on the endpoint's ModelLatency metric.
Why it's wrong here
Clarify explainability produces feature attributions that describe how inputs influence predictions; it does not evaluate a fairness constraint on a protected attribute. Alarming on ModelLatency monitors performance, not bias. This combination would not detect a fairness violation, so it fails the compliance requirement regardless of how quickly latency alarms fire.
- ✗
Create a SageMaker Model Monitor data quality job with a custom metric that flags predictions where the protected attribute equals a specific value, and alarm on that metric.
Why it's wrong here
A data quality job computes statistics and constraints on input features, and a custom metric could count records for a protected value, but that measures data distribution rather than outcome fairness. It cannot determine whether predictions violate the fairness constraint across groups. Detecting a fairness violation requires bias metrics computed against ground truth or predicted labels, which this approach lacks.
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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
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.