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MLA-C01 Practice Question: A data science team deployed a model on Amazon…
A data science team deployed a model on Amazon SageMaker and enabled Model Monitor to detect data drift. After a week, they receive alerts indicating that the distribution of a key feature has shifted significantly. However, the model's accuracy on the recent production data remains high. Which action should the team take next?
⚠ Common exam trap
Test-takers frequently assume data drift always implies model degradation, but the exam tests the understanding that drift can be benign and requires root-cause analysis before any action.
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
✓
Investigate the root cause of the drift as it may be benign or may lead to future degradation.
Data drift does not always immediately impact model accuracy; the drift may be benign (e.g., a shift in a non-predictive feature) or may indicate a precursor to future degradation. Amazon SageMaker Model Monitor detects distribution shifts using statistical tests like Kolmogorov-Smirnov or Chi-squared, but the team must investigate the root cause—such as changes in data collection, seasonal patterns, or upstream pipeline issues—before taking corrective action. Disabling alerts or retraining blindly could mask underlying problems or waste resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable the data drift alert since accuracy is not affected.
Why it's wrong here
Disabling alerts ignores potential future degradation; drift should be monitored even if accuracy is currently stable.
- ✗
Increase the sample size for monitoring to reduce false positives.
Why it's wrong here
The drift is likely real based on the alert; increasing sample size may not address the root cause.
- ✗
Retrain the model immediately because data drift always degrades performance.
Why it's wrong here
Data drift does not always cause immediate accuracy loss; retraining without investigation may be premature and wasteful.
- ✓
Investigate the root cause of the drift as it may be benign or may lead to future degradation.
Why this is correct
Investigating helps understand if the drift is meaningful; it could be benign or a leading indicator of future issues.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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