AI0-001 Machine Learning and Deep Learning Practice Question
An e-commerce company uses a gradient boosting model to forecast daily sales. Recently, the model's predictions have become less accurate, showing a significant drop in R-squared on validation data. The data scientist checks for data drift but finds no significant changes in feature distributions. The model was trained on data from the past 24 months and is retrained monthly. Upon inspecting the feature importance, the data scientist notices that the top feature 'promotion_flag' has decreased in importance over time. What is the most likely cause of the performance degradation, and what should be done?
⚠ Common exam trap
The trap is that candidates may confuse concept drift with data drift or overfitting, but the key clue is the change in feature importance over time without changes in feature distributions, pointing to concept drift rather than other issues.
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
✓
Concept drift has occurred; retrain the model more frequently with recent data only, or use an online learning approach
The model's performance degradation, despite no data drift in feature distributions, is likely due to concept drift—the relationship between features and the target variable has changed over time. The decreasing importance of 'promotion_flag' suggests that promotions no longer influence sales as they once did. Retraining more frequently with recent data or using online learning can help the model adapt to the new concept.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model is overfitting to historical promotions; apply more regularization
Why it's wrong here
A drop in 'promotion_flag' importance with stable feature distributions points to concept drift: the relationship between promotions and sales has changed, not the inputs. Regularisation constrains model complexity to close a train-validation gap, which is absent here. It would suit genuine overfitting, where training R-squared far exceeds validation.
- ✓
Concept drift has occurred; retrain the model more frequently with recent data only, or use an online learning approach
Why this is correct
Stable feature distributions but a shifting feature-target relationship indicate concept drift, not data drift. The declining importance of promotion_flag shows the old mapping no longer holds, so retraining on recent data or adopting online learning restores accuracy.
- ✗
The model's hyperparameters need tuning; perform a grid search
Why it's wrong here
Hyperparameter tuning cannot restore a relationship the model no longer captures: 'promotion_flag' losing importance while feature distributions stay stable points to concept drift, where P(y|X) changes, not the parameter search space. Grid search suits initial model selection or optimisation, not a temporal shift in the target relationship.
- ✗
The promotion_flag feature is leaking future information; remove it
Why it's wrong here
Leakage would inflate validation scores, not degrade them, and would have shown from the start. Declining promotion_flag importance alongside stable feature distributions points to concept drift, where the feature-target relationship changed, requiring retraining or feature review.
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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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