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?
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
(overfitting to promotions) does not explain the drop over time. Option C (hyperparameter tuning) is unlikely to fix the temporal change. Option D (leakage) would have caused issues from the start. Option B correctly identifies concept drift (changing relationship) and suggests retraining more frequently or using online learning to adapt.
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
Overfitting would cause poor generalization from the start, not a gradual decline.
- ✓
Concept drift has occurred; retrain the model more frequently with recent data only, or use an online learning approach
Why this is correct
Concept drift changes the relationship between features and target; frequent retraining adapts to new patterns.
- ✗
The model's hyperparameters need tuning; perform a grid search
Why it's wrong here
Hyperparameter tuning may not address the underlying temporal change.
- ✗
The promotion_flag feature is leaking future information; remove it
Why it's wrong here
Leakage would cause overly optimistic performance initially, not a gradual decline.
About these practice questions
One of 754 original AI0-001 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.