AI0-001 Machine Learning and Deep Learning Practice Question
An e-commerce company deploys a deep learning model for product recommendation. After a new data pipeline is implemented, the model's online performance degrades: recall drops by 20% and the click-through rate decreases. The data scientists suspect data drift. They compare the distribution of the input features between the training data and recent production data. The Kolmogorov-Smirnov test shows significant differences for two numerical features (price and rating). The team also notices that the frequency of categorical feature 'category' has changed. Which of the following is the MOST appropriate first step? A. Immediately retrain the model on all available data including new production data. B. Roll back to the previous data pipeline and investigate the root cause of drift. C. Use feature selection to remove the drifting features and retrain. D. Implement a monitoring dashboard to track drift over time and set up alerts.
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
✓
Roll back to the previous data pipeline and investigate the root cause of drift.
Since the drift occurred after a pipeline change, rolling back and investigating the root cause is the most prudent first step before making model changes. Retraining on drifted data (A) might incorporate a faulty distribution. Removing drifting features (C) could lose important information and may not fully address the issue. Implementing monitoring (D) is useful for long-term but does not address the immediate degradation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement a monitoring dashboard to track drift over time and set up alerts.
Why it's wrong here
Monitoring is proactive but does not recover the immediate performance drop; it should be done after resolving the issue.
- ✓
Roll back to the previous data pipeline and investigate the root cause of drift.
Why this is correct
Rolling back restores the previous stable distribution; investigating the root cause prevents recurrence.
- ✗
Use feature selection to remove the drifting features and retrain.
Why it's wrong here
Removing drifting features may discard important information; the drift might be due to a pipeline error that can be fixed.
- ✗
Immediately retrain the model on all available data including new production data.
Why it's wrong here
Retraining on drifted data without understanding the cause may encode faulty patterns and not fix the underlying issue.
About these practice questions
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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.