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MLA-C01 Practice Question: A data science team detects that a deployed…
A data science team detects that a deployed model's prediction accuracy is degrading over time due to concept drift. They need to implement a retraining strategy. Which THREE actions are recommended best practices for handling concept drift?
⚠ Common exam trap
Test-takers frequently confuse 'detecting drift' with 'responding to drift' and incorrectly choose automatic rollback (Option A) as a best practice, when in reality rollback is a risky operation that should be evaluated carefully, not automated blindly.
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
✓
Monitor prediction quality using ground truth labels when available.
Monitoring prediction quality using ground truth labels is a fundamental best practice for detecting concept drift. When ground truth labels are available, you can directly measure the model's accuracy over time, which provides the most reliable signal for drift. SageMaker Model Monitor can be configured to capture ground truth data and compare it against predictions to generate quality metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Automatically roll back to a previous model version upon drift detection.
Why it's wrong here
Rolling back does not address concept drift; the previous model may also be outdated.
- ✓
Monitor prediction quality using ground truth labels when available.
Why this is correct
Correct. Ground truth labels enable direct accuracy monitoring.
- ✗
Retrain the model on a fixed schedule regardless of performance.
Why it's wrong here
Fixed schedules are inefficient and may not align with drift patterns.
- ✓
Incrementally update the model with new data using SageMaker Pipelines.
Why this is correct
Correct. Incremental learning adapts to new patterns without full retraining.
- ✓
Use SageMaker Model Monitor to detect drift and trigger retraining.
Why this is correct
Correct. Model Monitor can automate drift detection and initiate retraining pipelines.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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