AI0-001 AI Implementation and Operations Practice Question
Exhibit
Refer to the exhibit. Model: logistic_regression_v1 Features: ['age', 'income', 'loan_amount', 'credit_score'] Training accuracy: 0.87 Test accuracy: 0.85 Deployment metrics (last 24 hours): - Accuracy: 0.72 - Precision: 0.68 - Recall: 0.81 - F1: 0.74 Feature distribution shift detected for 'income' (p < 0.05).
Based on the exhibit, what is the most likely cause of the accuracy drop?
⚠ Common exam trap
CompTIA often tests the distinction between data drift and model overfitting by presenting a sudden accuracy drop after stable performance, leading candidates to incorrectly attribute it to overfitting when the exhibit clearly shows a distribution shift in a specific feature.
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
✓
Data drift in the 'income' feature has caused the model to become less accurate.
The exhibit shows a sudden and sustained drop in model accuracy coinciding with a shift in the distribution of the 'income' feature. This is a classic symptom of data drift, where the statistical properties of the input feature change over time, causing the model's learned patterns to no longer match the production data. Option B correctly identifies this as the most likely cause because the model was trained on a prior income distribution and is now encountering values outside that range.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A required feature is missing from the production data pipeline.
Why it's wrong here
No mention of missing features; all features are present.
- ✓
Data drift in the 'income' feature has caused the model to become less accurate.
Why this is correct
The detected distribution shift for 'income' indicates data drift, a common cause of performance degradation.
- ✗
The model was overfitted to the training data.
Why it's wrong here
Training and test accuracy are close (0.87 vs 0.85), so overfitting is unlikely.
- ✗
The model's confidence threshold needs to be adjusted.
Why it's wrong here
Adjusting threshold changes precision-recall trade-off but does not fix the underlying drift.
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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.