- A
Run offline evaluation on a holdout dataset to confirm accuracy
Why wrong: Offline accuracy is already high; the issue is online performance, which requires online metrics.
- B
Set up an A/B experiment comparing the model's recommendations against a baseline
A/B testing validates the model's real-world performance and identifies issues.
- C
Retrain the model on the most recent three months of data to capture recent trends
User preferences may have shifted; retraining on recent data addresses concept drift.
- D
Check the distribution of predictions versus the training set to detect drift
Monitoring prediction drift helps identify if the model is seeing different inputs than during training.
- E
Increase the training dataset size by including data from two years ago
Why wrong: Older data may be less relevant and could dilute recent patterns.
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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)
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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).
Detailed technical explanation
How to think about this question
Concept drift occurs when the joint distribution P(X,Y) changes over time; in e-commerce, user preferences and product popularity shift seasonally or due to trends. Retraining on recent data (Option C) helps the model adapt to non-stationary environments, while checking prediction distribution (Option D) can reveal covariate shift (e.g., new products or user segments) that offline metrics miss. A/B testing (Option B) is the gold standard for online validation because it controls for temporal confounders like marketing campaigns.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PMLE question test?
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set up an A/B experiment comparing the model's recommendations against a baseline — Option B is correct because 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 30, 2026
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