MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning engineer is setting up model quality monitoring for a binary classification model. They have ground truth labels available in Amazon S3. Which TWO steps are required to configure model quality monitoring? (Choose two.)
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 a schedule for the monitoring job to run at regular intervals
A baseline must be computed from training data and ground truth, and a schedule for monitoring jobs must be defined. The monitoring job then compares production predictions against ground truth.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a CloudWatch Alarm for accuracy degradation
Why it's wrong here
CloudWatch Alarms can be set up after monitoring, but they are not a required step for configuring the monitoring itself.
- ✗
Use Amazon SageMaker Clarify for bias detection
Why it's wrong here
Clarify is for bias monitoring, not model quality.
- ✓
Create a schedule for the monitoring job to run at regular intervals
Why this is correct
A schedule defines how often the monitoring job runs to compare predictions against ground truth.
- ✓
Create a baseline for model quality using training data and ground truth labels
Why this is correct
A baseline establishes expected performance metrics (e.g., accuracy, precision) for comparison.
- ✗
Enable data capture on the endpoint to collect predictions
Why it's wrong here
Data capture is needed for predictions, but it is set up when creating the endpoint, not as part of configuring model quality monitoring.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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