This PMLE practice question tests your understanding of monitoring ml solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
You have deployed a regression model that predicts house prices. Over the past month, the model's predictions have been consistently too high. You suspect data drift in the input features. Which monitoring metric should you prioritize to confirm this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Monitor feature distribution drift using a divergence metric like Jensen-Shannon divergence
Option B is correct because the question describes a scenario where predictions are consistently too high, which is a symptom of data drift—a change in the distribution of input features. Monitoring feature distribution drift using a divergence metric like Jensen-Shannon divergence directly measures whether the input data has shifted from the training distribution, which would cause the model to make biased predictions. This is the most direct way to confirm data drift in the input features.
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.
Prediction drift indicates output change but not which feature drifted.
✓
Monitor feature distribution drift using a divergence metric like Jensen-Shannon divergence
Why this is correct
Feature drift measures input distribution change.
Related concept
Read the scenario before looking for a memorised answer.
✗
Monitor feature attribution drift using SHAP values
Why it's wrong here
Feature attribution drift measures impact on predictions, not input distribution.
✗
Monitor residual distribution drift
Why it's wrong here
Residuals measure error, not input drift.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between monitoring prediction drift (output) and feature drift (input), trapping candidates who assume that a change in predictions automatically implies data drift without verifying the input distributions.
Trap categories for this question
Command / output trap
Prediction drift indicates output change but not which feature drifted.
Detailed technical explanation
How to think about this question
Jensen-Shannon divergence is a symmetric and bounded version of Kullback-Leibler divergence, making it suitable for comparing two probability distributions (e.g., training vs. current feature distributions). In practice, you would compute JS divergence per feature or across the joint feature space; a significant increase indicates drift. Real-world scenarios like seasonal housing market shifts (e.g., summer vs. winter) can cause feature drift that degrades model accuracy, and JS divergence helps detect this before predictions become unreliable.
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.
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Monitor feature distribution drift using a divergence metric like Jensen-Shannon divergence — Option B is correct because the question describes a scenario where predictions are consistently too high, which is a symptom of data drift—a change in the distribution of input features. Monitoring feature distribution drift using a divergence metric like Jensen-Shannon divergence directly measures whether the input data has shifted from the training distribution, which would cause the model to make biased predictions. This is the most direct way to confirm data drift in the input features.
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.
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Question Discussion
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