AI0-001 AI Models and Data Engineering Practice Question
An e-commerce company deploys a model to recommend products to users. The recommendation system uses collaborative filtering based on user-item interaction history. After deployment, the model shows decreasing click-through rates (CTR) over time. The data engineer notices that the model was trained on data from the past six months and is retrained daily. However, the trend suggests that user preferences are shifting more rapidly than expected. The engineer suspects that the model is suffering from distribution drift. Which approach should the engineer implement to adapt the model more quickly to changing user behavior?
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
✓
Switch to an online learning algorithm that updates the model after each user click
Online learning algorithms update the model incrementally with each new user click, allowing it to rapidly adapt to shifting user preferences. This directly addresses distribution drift caused by fast-changing behaviors. Option A is wrong because increasing retraining to once per week reduces update frequency, making the model slower to adapt. Option C is wrong because adding complexity does not solve distribution drift and risks overfitting. Option D is wrong because training on only the last week may produce a noisy model and does not provide a mechanism for continuous adaptation.
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 retraining period to once per week to reduce computational cost
Why it's wrong here
Less frequent retraining would make the model less responsive to drift.
- ✓
Switch to an online learning algorithm that updates the model after each user click
Why this is correct
Online learning continuously adapts to new data, capturing shifts in user preferences promptly.
- ✗
Increase the model complexity by adding more features and layers
Why it's wrong here
Complexity does not address drift; it may even exacerbate overfitting.
- ✗
Use only the last week of data for training to focus on recent trends
Why it's wrong here
Too little data may lead to noise and overfitting on short-term fluctuations.
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.