- A
Retrain the model daily with the latest data to adapt to changing patterns.
Why wrong: Retraining is a fix, not a diagnostic; the team should first understand why MAE increased.
- B
Monitor prediction residuals and compute serving-time MAE over sliding windows.
Directly tracking MAE on serving data over time is the most straightforward diagnostic for performance degradation.
- C
Compare the distribution of training labels with serving labels using a two-sample t-test.
Why wrong: Label distribution shift can cause MAE increase, but monitoring serving MAE is more direct and actionable.
- D
Monitor input feature distributions for drift using the Kolmogorov-Smirnov test.
Why wrong: Input drift detection is useful but does not directly measure prediction error; the complaint is about MAE increase.
PMLE Monitoring ML solutions Practice Question
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.
A data science team deploys a regression model to predict house prices. After one month, the mean absolute error (MAE) on the serving data increases by 20% compared to the test set. Which monitoring strategy should the team implement first to diagnose the issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Monitor prediction residuals and compute serving-time MAE over sliding windows.
Option B is correct because the first step in diagnosing a 20% MAE increase on serving data is to monitor prediction residuals over sliding windows. This directly tracks how model errors evolve in production, allowing the team to detect whether performance degradation is sudden or gradual, and to correlate it with specific time windows or data slices. Computing serving-time MAE on sliding windows provides an immediate, interpretable signal of model health without assuming the root cause.
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.
- ✗
Retrain the model daily with the latest data to adapt to changing patterns.
Why it's wrong here
Retraining is a fix, not a diagnostic; the team should first understand why MAE increased.
- ✓
Monitor prediction residuals and compute serving-time MAE over sliding windows.
Why this is correct
Directly tracking MAE on serving data over time is the most straightforward diagnostic for performance degradation.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Compare the distribution of training labels with serving labels using a two-sample t-test.
Why it's wrong here
Label distribution shift can cause MAE increase, but monitoring serving MAE is more direct and actionable.
- ✗
Monitor input feature distributions for drift using the Kolmogorov-Smirnov test.
Why it's wrong here
Input drift detection is useful but does not directly measure prediction error; the complaint is about MAE increase.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that the first step in diagnosing model degradation is to check for data drift (Option D), when in fact the correct first step is to confirm and quantify the performance drop itself using serving-time metrics like sliding-window MAE.
Detailed technical explanation
How to think about this question
Monitoring prediction residuals over sliding windows involves computing the MAE on a rolling basis (e.g., hourly or daily windows) and comparing it against a baseline from the test set. This approach leverages the fact that residuals capture both bias and variance changes in real time, and can be visualized with control charts (e.g., Shewhart or CUSUM) to detect statistically significant degradation. In practice, a 20% MAE increase might stem from a subtle shift in a single feature like 'year_built' that the KS test on all features might miss, but residual monitoring would flag the performance drop immediately.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
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 prediction residuals and compute serving-time MAE over sliding windows. — Option B is correct because the first step in diagnosing a 20% MAE increase on serving data is to monitor prediction residuals over sliding windows. This directly tracks how model errors evolve in production, allowing the team to detect whether performance degradation is sudden or gradual, and to correlate it with specific time windows or data slices. Computing serving-time MAE on sliding windows provides an immediate, interpretable signal of model health without assuming the root cause.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 30, 2026
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.
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