hardMultiple SelectObjective-mapped
PMLE Practice Question: An e-commerce company uses a recommendation model…
An e-commerce company uses a recommendation model that suggests products based on user browsing history. The model was trained on data from the past year and has high accuracy on the test set. However, after deployment, the click-through rate (CTR) on recommendations is much lower than expected. Which three steps should the data scientist take to diagnose and improve the model? (Choose THREE)
⚠ Common exam trap
Google Cloud often tests the misconception that high offline accuracy guarantees online success, ignoring that offline metrics can be misleading due to distribution shift, feedback loops, or mismatched optimization objectives (e.g., accuracy vs. CTR).
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
✓
Set up an A/B experiment comparing the model's recommendations against a baseline
An A/B experiment directly measures the model's real-world impact by comparing its CTR against a baseline (e.g., random or popularity-based recommendations). This isolates the model's performance from confounding factors like seasonality or user behavior changes, providing a causal estimate of its effectiveness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run offline evaluation on a holdout dataset to confirm accuracy
Why it's wrong here
Offline accuracy is already high; the issue is online performance, which requires online metrics.
- ✓
Set up an A/B experiment comparing the model's recommendations against a baseline
Why this is correct
A/B testing validates the model's real-world performance and identifies issues.
- ✓
Retrain the model on the most recent three months of data to capture recent trends
Why this is correct
User preferences may have shifted; retraining on recent data addresses concept drift.
- ✓
Check the distribution of predictions versus the training set to detect drift
Why this is correct
Monitoring prediction drift helps identify if the model is seeing different inputs than during training.
- ✗
Increase the training dataset size by including data from two years ago
Why it's wrong here
Older data may be less relevant and could dilute recent patterns.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.