MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A team is using SageMaker Clarify to detect bias drift in a deployed model's predictions. They run weekly bias monitoring jobs. The team wants to be notified when the bias metric for a sensitive feature exceeds a threshold. What is the most efficient method to achieve this?
⚠ Common exam trap
Many candidates confuse SageMaker Clarify's bias monitoring with SageMaker Model Monitor's built-in bias drift capabilities, assuming that Clarify automatically creates CloudWatch metrics or integrates with SNS, when in fact it only outputs to S3 and requires a custom pipeline for metric extraction and alerting.
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
✓
After each Clarify job, run a custom Lambda that parses the report and publishes a custom CloudWatch metric; create an alarm on that metric
SageMaker Clarify bias monitoring jobs output a JSON report to S3 but do not natively publish CloudWatch metrics. By using a custom Lambda to parse the report and publish a custom CloudWatch metric, you can then create a CloudWatch alarm that triggers notifications when the bias metric exceeds a threshold. This is the most efficient automated method because it leverages CloudWatch's native alarm and notification capabilities without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the Clarify monitoring job to send results to an SNS topic directly
Why it's wrong here
Clarify does not natively send to SNS; it writes reports to S3.
- ✓
After each Clarify job, run a custom Lambda that parses the report and publishes a custom CloudWatch metric; create an alarm on that metric
Why this is correct
This approach translates bias metrics into CloudWatch metrics for alarming.
- ✗
Manually review the bias report in SageMaker Studio each week
Why it's wrong here
Manual review is not automated or efficient.
- ✗
Use SageMaker Model Monitor - Bias Drift Monitor which automatically creates CloudWatch metrics
Why it's wrong here
Model Monitor's bias drift monitor does not automatically create CloudWatch metrics; it writes to S3.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.