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 company deploys a classification model on Vertex AI for loan approval. After a month, they notice the precision has dropped significantly. What should they 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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Check for data drift using Vertex AI Model Monitoring
Option C is correct because a sudden drop in precision indicates that the model's predictions are no longer aligning with the ground truth, which is a classic symptom of data drift. Vertex AI Model Monitoring can automatically detect drift in feature distributions or prediction output compared to a baseline, allowing you to identify the root cause before taking corrective action. Retraining or reverting without first diagnosing the drift could waste resources or mask the underlying issue.
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 with more data
Why it's wrong here
Retraining without diagnosis may waste resources if the issue is not data insufficiency.
✗
Increase the number of prediction nodes
Why it's wrong here
Increasing nodes addresses latency, not precision.
✓
Check for data drift using Vertex AI Model Monitoring
Why this is correct
Model monitoring is designed to detect drift, which could cause precision drop.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Revert to the previous model version
Why it's wrong here
Reverting might temporarily fix but doesn't identify root cause.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that any performance degradation should be immediately fixed by retraining or rolling back, rather than first diagnosing the cause through monitoring tools like Vertex AI Model Monitoring.
Detailed technical explanation
How to think about this question
Vertex AI Model Monitoring uses statistical tests like the Kolmogorov-Smirnov (K-S) test for numerical features and the chi-squared test for categorical features to compare serving data against a training baseline. It can also monitor prediction output drift via the Jensen-Shannon divergence. In practice, a bank might see precision drop because the economic profile of loan applicants changed (e.g., higher unemployment), and Model Monitoring would flag the drift in features like income or credit score before any retraining is attempted.
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: Check for data drift using Vertex AI Model Monitoring — Option C is correct because a sudden drop in precision indicates that the model's predictions are no longer aligning with the ground truth, which is a classic symptom of data drift. Vertex AI Model Monitoring can automatically detect drift in feature distributions or prediction output compared to a baseline, allowing you to identify the root cause before taking corrective action. Retraining or reverting without first diagnosing the drift could waste resources or mask the underlying issue.
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.
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Question Discussion
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