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.
You have deployed a classification model on Vertex AI Endpoints. The model's training data had a balanced class distribution, but over time, the production data has shifted such that one class appears 90% of the time. The model's overall accuracy remains high, but the recall for the minority class has dropped significantly. What is the best approach to detect and address this issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set up Vertex AI Model Monitoring to detect skew and drift, and retrain using a sliding window of recent data
Vertex AI Model Monitoring is specifically designed to detect skew and drift between training and serving data. In this scenario, the production data has shifted to 90% of one class, which is a clear case of data drift. By setting up monitoring, you can be alerted to this drift and then retrain the model using a sliding window of recent data, which adapts to the new distribution without requiring full retraining on the entire historical dataset. This approach directly addresses the root cause—the shift in class distribution—rather than just treating symptoms.
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 daily on the entire historical dataset
Why it's wrong here
Retraining on old data can dilute recent shift; better to use sliding window or weighted approach.
✓
Set up Vertex AI Model Monitoring to detect skew and drift, and retrain using a sliding window of recent data
Why this is correct
Model Monitoring detects skew/drift; retraining on recent data adapts to new distribution.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of replicas on the endpoint to reduce latency
Why it's wrong here
Scaling replicas improves throughput/latency but does not affect model accuracy or drift.
✗
Adjust the decision threshold to improve minority class recall
Why it's wrong here
Adjusting threshold helps recall but does not address the underlying data drift; performance may degrade further.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between monitoring/detection (Model Monitoring) and reactive fixes (threshold tuning), where candidates mistakenly choose a quick fix like adjusting the decision threshold instead of addressing the root cause of data drift.
Detailed technical explanation
How to think about this question
Vertex AI Model Monitoring uses statistical tests like the Kolmogorov-Smirnov test for numerical features and the chi-squared test for categorical features to detect drift. When drift is detected, you can trigger a retraining pipeline using a sliding window of recent data, which ensures the model is trained on the most representative sample of the current production distribution. This is critical in scenarios like fraud detection, where class distributions can shift rapidly due to seasonal patterns or adversarial behavior.
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: Set up Vertex AI Model Monitoring to detect skew and drift, and retrain using a sliding window of recent data — Vertex AI Model Monitoring is specifically designed to detect skew and drift between training and serving data. In this scenario, the production data has shifted to 90% of one class, which is a clear case of data drift. By setting up monitoring, you can be alerted to this drift and then retrain the model using a sliding window of recent data, which adapts to the new distribution without requiring full retraining on the entire historical dataset. This approach directly addresses the root cause—the shift in class distribution—rather than just treating symptoms.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 →
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.
This PDE 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 PDE 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.