- A
Investigate the input feature distributions for the recent serving requests to identify if data drift is the underlying cause of the prediction drift.
By checking input feature distributions, the engineer can confirm whether data drift is present, which commonly causes prediction drift even if accuracy remains temporarily stable.
- B
Increase the prediction drift alert threshold to 0.4 to reduce the number of false alerts.
Why wrong: Increasing the threshold only hides the symptom and does not address the shift in predictions.
- C
Retrain the model using the latest three months of data to incorporate recent trends.
Why wrong: Retraining without understanding the cause may not address the root issue and could be unnecessary if the model is still accurate.
- D
Roll back to an earlier model version that had lower prediction drift.
Why wrong: The earlier model was trained on even older data and may not perform better on current data; also, accuracy is still acceptable, so rollback is not warranted.
Quick Answer
The answer is to investigate the input feature distributions for the recent serving requests to identify if data drift is the underlying cause of the prediction drift. This is correct because when investigating prediction drift with stable accuracy, the key technical distinction is between data drift and concept drift: a shift in prediction distribution without a corresponding drop in accuracy indicates that the input features have changed (data drift), while the relationship between features and labels remains intact. On the Google Professional Machine Learning Engineer exam, this scenario tests your ability to diagnose monitoring alerts by separating symptom from cause—a common trap is jumping to retrain the model when accuracy is fine, which wastes resources without addressing the root issue. Remember the memory tip: "Stable accuracy, drifting predictions? Check the inputs, not the labels."
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 retail company has deployed a machine learning model using Vertex AI Endpoints to predict inventory demand. The model was trained on data from the past two years and has been in production for six months. The team has enabled Vertex AI Model Monitoring to track prediction drift with an alert threshold of 0.2. Last week, they received an alert that the prediction drift score reached 0.35, exceeding the threshold. The engineer checks the monitoring dashboard and sees that the distribution of predictions has shifted noticeably compared to the training data. The engineer also notices that the model's accuracy metrics, computed from weekly ground truth data, have remained within acceptable range. What should the engineer do first?
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
Investigate the input feature distributions for the recent serving requests to identify if data drift is the underlying cause of the prediction drift.
The prediction drift alert indicates a shift in prediction distribution, but accuracy is stable. This suggests data drift (change in input features) rather than concept drift. The engineer should first investigate input feature distributions to confirm if data drift is the cause. Retraining (A) is premature without root cause analysis. Increasing the threshold (C) ignores the underlying issue. Rolling back (D) may not help if the previous version also suffers from the same data drift.
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.
- ✓
Investigate the input feature distributions for the recent serving requests to identify if data drift is the underlying cause of the prediction drift.
Why this is correct
By checking input feature distributions, the engineer can confirm whether data drift is present, which commonly causes prediction drift even if accuracy remains temporarily stable.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the prediction drift alert threshold to 0.4 to reduce the number of false alerts.
Why it's wrong here
Increasing the threshold only hides the symptom and does not address the shift in predictions.
- ✗
Retrain the model using the latest three months of data to incorporate recent trends.
Why it's wrong here
Retraining without understanding the cause may not address the root issue and could be unnecessary if the model is still accurate.
- ✗
Roll back to an earlier model version that had lower prediction drift.
Why it's wrong here
The earlier model was trained on even older data and may not perform better on current data; also, accuracy is still acceptable, so rollback is not warranted.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Investigate the input feature distributions for the recent serving requests to identify if data drift is the underlying cause of the prediction drift. — The prediction drift alert indicates a shift in prediction distribution, but accuracy is stable. This suggests data drift (change in input features) rather than concept drift. The engineer should first investigate input feature distributions to confirm if data drift is the cause. Retraining (A) is premature without root cause analysis. Increasing the threshold (C) ignores the underlying issue. Rolling back (D) may not help if the previous version also suffers from the same data drift.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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
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 →
Last reviewed: Jun 24, 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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