- A
Tune the alert threshold for 'transaction_amount' to 0.6 to avoid future false alarms.
Why wrong: Adjusting the threshold without investigation may simply silence a valid signal of changing model behavior.
- B
Retrain the model by increasing regularization to reduce the importance of the 'transaction_amount' feature.
Why wrong: Forcing the model to ignore a feature without understanding the cause may harm predictive performance and not address the underlying data shift.
- C
Investigate whether there has been a shift in the distribution of 'transaction_amount' values in the recent transaction data, which could explain the attribution change.
A distribution shift in the feature values can cause the model to rely more heavily on that feature, leading to higher attribution scores. Investigating this is the appropriate diagnostic step.
- D
Disable the feature attribution drift monitoring for 'transaction_amount' since the model accuracy is stable.
Why wrong: Accuracy stability does not guarantee that the model's decision-making process hasn't changed; disabling monitoring could allow compliance issues to go undetected.
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. 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.
A financial services company uses a custom deep learning model on Vertex AI to automatically approve or reject credit card transactions. The model is explainable using Vertex Explainable AI, and the company monitors feature attribution drift with thresholds defined per feature. Last week, the monitoring system flagged that the mean absolute attribution score for the 'transaction_amount' feature increased from 0.35 to 0.55. The overall model accuracy, measured on a daily batch of labeled transactions, has remained around 97%. The operations team is concerned about potential compliance issues due to changing model behavior. What should the data scientist do?
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 whether there has been a shift in the distribution of 'transaction_amount' values in the recent transaction data, which could explain the attribution change.
Option C is correct because a shift in the distribution of the 'transaction_amount' feature (e.g., due to seasonality or a new customer segment) can naturally cause its attribution score to change without indicating model degradation. Vertex Explainable AI computes feature attributions relative to the current data distribution; if the input values shift, the model's reliance on that feature may legitimately increase. Investigating the distribution shift is the first diagnostic step before adjusting thresholds or retraining, as stable accuracy does not rule out data drift that could lead to compliance issues.
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.
- ✗
Tune the alert threshold for 'transaction_amount' to 0.6 to avoid future false alarms.
Why it's wrong here
Adjusting the threshold without investigation may simply silence a valid signal of changing model behavior.
- ✗
Retrain the model by increasing regularization to reduce the importance of the 'transaction_amount' feature.
Why it's wrong here
Forcing the model to ignore a feature without understanding the cause may harm predictive performance and not address the underlying data shift.
- ✓
Investigate whether there has been a shift in the distribution of 'transaction_amount' values in the recent transaction data, which could explain the attribution change.
Why this is correct
A distribution shift in the feature values can cause the model to rely more heavily on that feature, leading to higher attribution scores. Investigating this is the appropriate diagnostic step.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Disable the feature attribution drift monitoring for 'transaction_amount' since the model accuracy is stable.
Why it's wrong here
Accuracy stability does not guarantee that the model's decision-making process hasn't changed; disabling monitoring could allow compliance issues to go undetected.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume stable accuracy means the model is fine, but the PMLE exam tests that feature attribution drift can indicate a change in model behavior that accuracy alone cannot detect, especially for compliance-sensitive applications.
Detailed technical explanation
How to think about this question
Vertex Explainable AI uses techniques like Integrated Gradients or Shapley value approximations to compute feature attributions, which are sensitive to the baseline (reference) input distribution. A shift in the feature distribution can alter the gradient path or the Shapley value estimates, causing attribution scores to change even if the model weights are unchanged. In practice, monitoring both prediction drift and feature attribution drift is essential because attribution drift can signal a change in the model's decision logic that accuracy metrics may not detect until a significant bias accumulates.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Monitoring ML solutions — study guide chapter
Learn the concepts, then practise the questions
- →
Monitoring ML solutions practice questions
Targeted practice on this topic area only
- →
All PMLE questions
506 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Scaling prototypes into ML models practice questions
Practise PMLE questions linked to Scaling prototypes into ML models.
Automating and orchestrating ML pipelines practice questions
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Collaborating within and across teams to manage data and models practice questions
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Architecting low-code ML solutions practice questions
Practise PMLE questions linked to Architecting low-code ML solutions.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Serving and scaling models practice questions
Practise PMLE questions linked to Serving and scaling models.
Monitoring ML solutions practice questions
Practise PMLE questions linked to Monitoring ML solutions.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 whether there has been a shift in the distribution of 'transaction_amount' values in the recent transaction data, which could explain the attribution change. — Option C is correct because a shift in the distribution of the 'transaction_amount' feature (e.g., due to seasonality or a new customer segment) can naturally cause its attribution score to change without indicating model degradation. Vertex Explainable AI computes feature attributions relative to the current data distribution; if the input values shift, the model's reliance on that feature may legitimately increase. Investigating the distribution shift is the first diagnostic step before adjusting thresholds or retraining, as stable accuracy does not rule out data drift that could lead to compliance issues.
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 →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.