AI0-001 Machine Learning and Deep Learning Practice Question
A retail company trains a gradient-boosted tree model to forecast weekly demand for 500 stores. After six months, forecast error rises sharply even though the model code and pipeline are unchanged. Store openings, promotions, and seasonality have shifted the underlying demand patterns. Which action best addresses the root cause?
⚠ Common exam trap
The trap here is treating degraded accuracy as a hyperparameter problem and tuning the existing model, when the real issue is that the data distribution has moved away from what the model learned.
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
✓
Schedule periodic retraining with recent data and monitor for data drift.
When a deployed model's error grows without code changes and the business environment has shifted, the cause is drift between training and production data. Retraining on recent data restores alignment with current patterns, and continuous drift monitoring provides early warning. This is the standard MLOps response to concept drift in forecasting systems.
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 number of boosting rounds in the existing model.
Why it's wrong here
Adding more boosting rounds only makes the model fit its original training data more closely; it cannot learn patterns that did not exist when the model was trained. Because the code and pipeline are unchanged and the data distribution has shifted, extra trees will not recover accuracy. This action also risks overfitting to stale historical relationships.
- ✗
Replace the gradient-boosted model with a deeper neural network.
Why it's wrong here
Changing model family does not fix drift; a neural network trained on the same stale data will also degrade as demand patterns shift. The problem is the data distribution, not the algorithm's capacity. This is an expensive change that leaves the root cause untouched and delays a correct fix.
- ✗
Lower the learning rate and re-evaluate on the original test set.
Why it's wrong here
The learning rate affects optimization during training, not the model's ability to generalize to shifted future data. Re-evaluating on the original test set is also misleading because that set reflects old demand patterns and will not reveal current drift. This action does not address the underlying distribution change.
- ✓
Schedule periodic retraining with recent data and monitor for data drift.
Why this is correct
The scenario describes concept and data drift: the relationship between features and demand has changed due to new stores, promotions, and seasonality. Periodic retraining on recent data lets the model relearn current patterns, while drift monitoring detects when retraining is needed. This directly targets the root cause rather than masking symptoms, and it fits a production forecasting pipeline.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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