- A
Compare the feature distribution in the training data with the recent serving data using statistical tests.
This diagnostic step helps understand the nature and extent of the drift.
- B
Retrain the model on the most recent data to incorporate the new distribution.
If drift is due to a real shift, retraining with recent data can improve performance.
- C
Increase the frequency of model monitoring checks to every hour.
Why wrong: More frequent monitoring does not address the cause of drift.
- D
Increase the sampling rate for prediction logging to ensure full data capture.
Why wrong: While helpful for analysis, it's not a direct corrective action for drift.
- E
Reduce the alert threshold to minimize false positives.
Why wrong: This would suppress legitimate alerts and not solve the drift issue.
Quick Answer
The correct actions are to first compare the training feature distribution with recent serving data using statistical tests like Kolmogorov-Smirnov in BigQuery, and then retrain the model on the most recent data to incorporate the new distribution. This two-step approach is essential because significant feature drift in Vertex AI means the model’s input distribution has shifted, degrading its predictive power; diagnosing the drift with statistical quantification confirms the severity, while retraining adapts the model to the current data reality. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of the MLOps lifecycle—specifically, how to respond to monitoring alerts without blindly retraining. A common trap is to immediately retrain without first verifying the drift’s statistical significance, which could waste resources on noise. Memory tip: “Verify then rectify”—always confirm drift with statistical tests before retraining.
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 financial services company has deployed a classification model on Vertex AI to detect fraudulent transactions. The model is monitored using Vertex AI Model Monitoring for skew and drift detection, and also logs predictions to BigQuery for analysis. After a month, the monitoring alerts show a significant drift in one feature (transaction_amount). Which TWO actions should the team take to diagnose and address this issue?
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
Compare the feature distribution in the training data with the recent serving data using statistical tests.
Option A is correct because comparing the feature distribution of the training data with recent serving data using statistical tests (e.g., Kolmogorov-Smirnov or Jensen-Shannon divergence) is the standard first step to quantify the drift and confirm it is statistically significant. This diagnostic action helps the team understand the nature and magnitude of the drift before deciding on remediation steps. Vertex AI Model Monitoring already performs such comparisons, but the team should independently verify the results in BigQuery to ensure accuracy.
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.
- ✓
Compare the feature distribution in the training data with the recent serving data using statistical tests.
Why this is correct
This diagnostic step helps understand the nature and extent of the drift.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Retrain the model on the most recent data to incorporate the new distribution.
Why this is correct
If drift is due to a real shift, retraining with recent data can improve performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the frequency of model monitoring checks to every hour.
Why it's wrong here
More frequent monitoring does not address the cause of drift.
- ✗
Increase the sampling rate for prediction logging to ensure full data capture.
Why it's wrong here
While helpful for analysis, it's not a direct corrective action for drift.
- ✗
Reduce the alert threshold to minimize false positives.
Why it's wrong here
This would suppress legitimate alerts and not solve the drift issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'detecting drift' with 'fixing drift' and immediately choose retraining (Option B) without first performing a diagnostic comparison, which is a critical step in the ML lifecycle per the PMLE exam's emphasis on systematic troubleshooting.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Model Monitoring uses a reference distribution (typically the training data) and computes a distance metric (e.g., L-infinity distance for skew, or Jensen-Shannon divergence for drift) on a sliding window of serving data. The alert triggers when the metric exceeds a user-defined threshold. In practice, a drift in transaction_amount could be due to seasonal spending patterns, a new product launch, or a data pipeline error (e.g., missing currency conversion). The team should also inspect the prediction logs in BigQuery for any data quality issues, such as null values or outliers, before deciding to retrain.
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
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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: Compare the feature distribution in the training data with the recent serving data using statistical tests. — Option A is correct because comparing the feature distribution of the training data with recent serving data using statistical tests (e.g., Kolmogorov-Smirnov or Jensen-Shannon divergence) is the standard first step to quantify the drift and confirm it is statistically significant. This diagnostic action helps the team understand the nature and magnitude of the drift before deciding on remediation steps. Vertex AI Model Monitoring already performs such comparisons, but the team should independently verify the results in BigQuery to ensure accuracy.
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
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Last reviewed: Jun 11, 2026
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