- A
A training-serving skew exists between the training pipeline and the serving endpoint.
Why wrong: Training-serving skew would cause feature mismatches, but the volume increase and prediction shift point to covariate shift.
- B
Concept drift has occurred, changing the relationship between features and churn.
Why wrong: Concept drift would typically degrade accuracy, which is stable here.
- C
The incoming data distribution has changed, e.g., due to a new marketing campaign attracting different customers.
This is covariate shift; the model sees inputs it wasn't trained on, leading to lower confidence predictions.
- D
Data leakage during training caused the model to overfit to historical patterns.
Why wrong: Data leakage would likely cause validation accuracy to drop when exposed to new data, not remain stable.
Quick Answer
The answer is covariate shift, which occurs when the distribution of incoming prediction data changes while the relationship between features and labels remains the same. In this scenario, a new marketing campaign likely attracted a different customer segment with inherently lower churn risk, causing the model to see a population shift that lowers predicted probabilities even though validation accuracy stays stable. On the Google Professional Machine Learning Engineer exam, this tests your ability to distinguish covariate shift from model degradation or data drift in production monitoring—a common trap is assuming accuracy drops always signal a broken model. Remember the memory tip: “Shift in X, not Y” means the input distribution changed, not the underlying prediction logic.
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 custom ML model on Vertex AI to predict customer churn. The model retrains weekly, and predictions are served via a Vertex AI endpoint. After a recent retraining, the monitoring dashboard shows a sudden increase in prediction requests but a decrease in predicted churn probabilities. The model's accuracy on the validation set remains stable. What is the most likely cause of the observed behavior?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
The incoming data distribution has changed, e.g., due to a new marketing campaign attracting different customers.
Option C is correct because a sudden increase in prediction requests alongside a decrease in predicted churn probabilities, while validation accuracy remains stable, indicates a shift in the incoming data distribution (covariate shift). This is typical when a new marketing campaign attracts a different customer segment that inherently has lower churn risk. The model itself hasn't degraded; it's simply seeing a different population than it was trained on, which changes the base rate of churn in the live traffic.
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.
- ✗
A training-serving skew exists between the training pipeline and the serving endpoint.
Why it's wrong here
Training-serving skew would cause feature mismatches, but the volume increase and prediction shift point to covariate shift.
- ✗
Concept drift has occurred, changing the relationship between features and churn.
Why it's wrong here
Concept drift would typically degrade accuracy, which is stable here.
- ✓
The incoming data distribution has changed, e.g., due to a new marketing campaign attracting different customers.
Why this is correct
This is covariate shift; the model sees inputs it wasn't trained on, leading to lower confidence predictions.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data leakage during training caused the model to overfit to historical patterns.
Why it's wrong here
Data leakage would likely cause validation accuracy to drop when exposed to new data, not remain stable.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between covariate shift (data distribution change) and concept drift (relationship change), trapping candidates who assume any change in predictions must be due to model degradation or data leakage.
Detailed technical explanation
How to think about this question
Covariate shift, as described in Option C, occurs when the distribution of input features (P(X)) changes while the conditional distribution of the target given features (P(Y|X)) remains the same. In Vertex AI, this can be detected by monitoring the prediction distribution drift using tools like Vertex AI Model Monitoring, which compares the serving data distribution to the training data distribution using statistical tests (e.g., Kolmogorov-Smirnov, Jensen-Shannon divergence). A real-world scenario is a seasonal promotion where the model sees mostly new, low-risk customers, causing the average churn probability to drop even though the model's per-customer predictions remain accurate.
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 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: The incoming data distribution has changed, e.g., due to a new marketing campaign attracting different customers. — Option C is correct because a sudden increase in prediction requests alongside a decrease in predicted churn probabilities, while validation accuracy remains stable, indicates a shift in the incoming data distribution (covariate shift). This is typical when a new marketing campaign attracts a different customer segment that inherently has lower churn risk. The model itself hasn't degraded; it's simply seeing a different population than it was trained on, which changes the base rate of churn in the live traffic.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 30, 2026
This PMLE 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 PMLE exam.
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