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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 the alert discards genuine distributional change; sustained drift can degrade accuracy later even while current metrics hold. It is tempting because high accuracy suggests stability, and would be correct only if the drift were confirmed as an expected, benign seasonal shift rather than an unexplained feature distribution change.
- ✗
Increase the sample size for monitoring to reduce false positives.
Why it's wrong here
Increasing the sampling rate cannot resolve a genuine distributional shift; Model Monitor compares live feature distributions against the baseline captured at deployment, so a real drift alert persists regardless of sample size. Larger samples are warranted when statistical noise from small batches triggers spurious violations, not when the feature distribution has actually moved.
- ✗
Retrain the model immediately because data drift always degrades performance.
Why it's wrong here
Drift in a feature distribution does not by itself prove degraded predictions; the stem states accuracy remains high, so retraining discards a working model. Retraining is warranted when monitored performance metrics fall, not merely when input distributions shift.
- ✓
Investigate the root cause of the drift as it may be benign or may lead to future degradation.
Why this is correct
Drift alerts flag distributional shift, not performance loss. Since accuracy stays high, the shift may be benign, but monitoring exists precisely to catch silent degradation before it harms outcomes. Investigating the root cause satisfies the need to determine whether the drift is harmless or a precursor to future accuracy decline.
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