easyMultiple ChoiceObjective-mapped
PMLE Practice Question: A company has a prototype ML model that works…
A company has a prototype ML model that works well on historical data, but when deployed to production, the model performance degrades over time. The data distribution shifts gradually. Which strategy should they implement to maintain model accuracy?
⚠ Common exam trap
Google Cloud often tests the misconception that overfitting or model complexity is the primary cause of production degradation, leading candidates to choose regularization or more complex architectures instead of recognizing that distribution shift requires data freshness.
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
✓
Implement a retraining pipeline that periodically retrains the model on recent data.
Gradual data distribution shifts (concept drift) require the model to adapt to new patterns over time. A retraining pipeline that periodically retrains on recent data ensures the model remains aligned with the current production distribution, directly addressing the degradation caused by drift without relying on static historical data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the regularization strength to prevent overfitting.
Why it's wrong here
Regularization reduces overfitting but does not address distribution shift.
- ✗
Increase the amount of training data by using more historical records.
Why it's wrong here
Using more historical data does not address the shift in data distribution.
- ✓
Implement a retraining pipeline that periodically retrains the model on recent data.
Why this is correct
Periodic retraining with fresh data helps the model adapt to gradual distribution shifts.
- ✗
Switch to a more complex model architecture to better capture patterns.
Why it's wrong here
A more complex model may overfit to old patterns and not generalize to new data.
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