The correct answer is to retrain the model with more recent data. This is the standard remediation because Vertex AI Model Monitoring detected a drift in the 'age' feature, meaning the distribution of production data has shifted away from the training data, causing the model’s predictions to become less accurate. Retraining with fresh data realigns the model to the current distribution, directly addressing the alert. On the Google Professional Data Engineer exam, this scenario tests your understanding of model monitoring and drift remediation—a common trap is to overcomplicate the fix by suggesting feature engineering or threshold adjustments when the root cause is simply stale data. Remember the memory tip: when you see a drift alert, think “refresh the data, not the architecture.”
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. 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.
Refer to the exhibit. This log entry was generated by Vertex AI Model Monitoring for a production model. What should the data engineer do to address this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Retrain the model with more recent data
Option B is correct because Vertex AI Model Monitoring detected a drift in the 'age' feature, indicating that the production data distribution has shifted from the training data. Retraining the model with more recent data aligns the model with the current data distribution, mitigating the drift and maintaining prediction accuracy. This is the standard remediation for model drift in production ML systems.
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.
✗
Increase the drift threshold to 0.9 to suppress alerts
Why it's wrong here
Masking the problem is not a solution.
✓
Retrain the model with more recent data
Why this is correct
Addresses the root cause by adapting to data shift.
Related concept
Read the scenario before looking for a memorised answer.
✗
Deploy a new model version trained on the original dataset
Why it's wrong here
Original dataset does not reflect current data.
✗
Disable monitoring for the 'age' feature
Why it's wrong here
Removes visibility.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that adjusting thresholds or disabling monitoring is a valid fix for drift, when the correct action is always to retrain the model with current data.
Detailed technical explanation
How to think about this question
Vertex AI Model Monitoring uses statistical tests like the Kolmogorov-Smirnov (K-S) test or Jensen-Shannon divergence to compare feature distributions between training and serving data. When drift is detected, the model's predictions become unreliable because the learned relationships no longer hold. In practice, retraining should be automated via a pipeline that triggers on drift alerts, using the most recent labeled data to ensure the model adapts to real-world shifts.
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.
Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Retrain the model with more recent data — Option B is correct because Vertex AI Model Monitoring detected a drift in the 'age' feature, indicating that the production data distribution has shifted from the training data. Retraining the model with more recent data aligns the model with the current data distribution, mitigating the drift and maintaining prediction accuracy. This is the standard remediation for model drift in production ML systems.
What should I do if I get this PDE 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.
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Question Discussion
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