- A
Retrain the model using the latest client data to adapt to any changes in preprocessing.
Why wrong: Retraining without understanding the root cause may not solve the issue and could introduce new problems.
- B
Roll back to a previous model version that was known to work well and disable automatic retraining.
Why wrong: This is a temporary fix and does not address the underlying issue; also may miss out on improvements.
- C
Ask the developers to provide the exact preprocessing code and manually compare it with the training pipeline's preprocessing.
Why wrong: Manual comparison is error-prone and not scalable; automated monitoring is preferred.
- D
Enable Vertex AI Model Monitoring for feature attribution and set up alerting on skew detection.
Model Monitoring can detect training-serving skew by comparing feature distributions; this would catch preprocessing changes effectively.
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.
Your company has deployed a machine learning model on Vertex AI Endpoint to serve real-time predictions for a mobile application. The model was trained using TensorFlow and the prediction requests include raw images that are preprocessed by the client before sending. Recently, the application developers reported that the predictions are becoming less accurate over time. They suspect the issue is related to changes in the client-side preprocessing code. You need to verify this hypothesis and monitor for future regressions. What should you 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
Enable Vertex AI Model Monitoring for feature attribution and set up alerting on skew detection.
Option D is correct because Vertex AI Model Monitoring can automatically detect skew between the training data distribution and the live prediction data distribution. By enabling feature attribution and alerting on skew detection, you can quantitatively verify whether changes in client-side preprocessing are causing prediction drift, without manual code comparison or disruptive rollbacks.
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 using the latest client data to adapt to any changes in preprocessing.
Why it's wrong here
Retraining without understanding the root cause may not solve the issue and could introduce new problems.
- ✗
Roll back to a previous model version that was known to work well and disable automatic retraining.
Why it's wrong here
This is a temporary fix and does not address the underlying issue; also may miss out on improvements.
- ✗
Ask the developers to provide the exact preprocessing code and manually compare it with the training pipeline's preprocessing.
Why it's wrong here
Manual comparison is error-prone and not scalable; automated monitoring is preferred.
- ✓
Enable Vertex AI Model Monitoring for feature attribution and set up alerting on skew detection.
Why this is correct
Model Monitoring can detect training-serving skew by comparing feature distributions; this would catch preprocessing changes effectively.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly choose to manually compare preprocessing code or retrain the model, but the correct approach is to use Vertex AI Model Monitoring to automatically detect skew between training and live data distributions, which is a standard MLOps practice.
Detailed technical explanation
How to think about this question
Vertex AI Model Monitoring uses a reference distribution (typically the training data) and computes a distance metric (e.g., Jensen-Shannon divergence or L-infinity distance) on feature attributions to detect skew. It can alert on both training-serving skew and drift, and it integrates with Cloud Monitoring for automated incident response. In practice, even a small change in image normalization (e.g., switching from [0,1] to [-1,1] scaling) can cause significant feature attribution skew that this monitoring catches.
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.
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FAQ
Questions learners often ask
What does this PDE question test?
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: Enable Vertex AI Model Monitoring for feature attribution and set up alerting on skew detection. — Option D is correct because Vertex AI Model Monitoring can automatically detect skew between the training data distribution and the live prediction data distribution. By enabling feature attribution and alerting on skew detection, you can quantitatively verify whether changes in client-side preprocessing are causing prediction drift, without manual code comparison or disruptive rollbacks.
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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Last reviewed: Jul 4, 2026
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