- A
The distribution of input features has shifted significantly, causing the model to produce incorrect probabilities.
Why wrong: Feature drift can cause poor performance, but the problem statement does not mention feature drift; calibration degradation is specifically addressed.
- B
The model overfits to noise in the training data, leading to poor generalization.
Why wrong: Overfitting would show poor test set performance, but the test set had good Brier score.
- C
The production data has a different class imbalance than the training data, causing the model to be biased toward the majority class.
Why wrong: Class imbalance alone does not explain miscalibration; the model's probability estimates can still be calibrated if the imbalance is accounted for.
- D
The relationship between features and the target has changed (concept drift), causing the model's probability estimates to be misaligned with the true probabilities.
Concept drift changes the conditional distribution P(Y|X), which directly affects calibration.
Quick Answer
The answer is concept drift caused by prior probability shift. This is correct because the model was calibrated on a test set with a 5% fraud rate, but production sees only 0.5% fraud; the change in class distribution directly misaligns the model’s predicted probabilities from the true posterior probabilities, inflating the Brier score from 0.02 to 0.15. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding that calibration degradation due to prior probability shift is a specific form of concept drift where the base rate of the target changes, not the feature-target relationship itself—a common trap is confusing it with covariate shift. Remember the mnemonic: “Prior shift punishes probabilities,” meaning a shift in the prior class distribution will always degrade a model’s probability estimates unless recalibrated.
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 financial services firm deploys a binary classification model for fraud detection. The model's precision is 0.95 and recall is 0.60 on the test set. After deployment, the fraud rate in production is 0.5% compared to 5% in the test set. The model shows good calibration on the test set (Brier score 0.02) but poor calibration in production (Brier score 0.15). What is the most likely explanation for the calibration degradation?
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 relationship between features and the target has changed (concept drift), causing the model's probability estimates to be misaligned with the true probabilities.
The model's calibration degrades in production despite being well-calibrated on the test set, which had a 5% fraud rate, while production has a 0.5% fraud rate. This shift in class imbalance (prior probability shift) directly affects the model's probability estimates because the model's predicted probabilities are conditional on the training distribution. Option D is correct because concept drift—specifically a change in the base rate of fraud—causes the model's probability estimates to no longer reflect the true posterior probabilities in production, leading to a higher Brier score.
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.
- ✗
The distribution of input features has shifted significantly, causing the model to produce incorrect probabilities.
Why it's wrong here
Feature drift can cause poor performance, but the problem statement does not mention feature drift; calibration degradation is specifically addressed.
- ✗
The model overfits to noise in the training data, leading to poor generalization.
Why it's wrong here
Overfitting would show poor test set performance, but the test set had good Brier score.
- ✗
The production data has a different class imbalance than the training data, causing the model to be biased toward the majority class.
Why it's wrong here
Class imbalance alone does not explain miscalibration; the model's probability estimates can still be calibrated if the imbalance is accounted for.
- ✓
The relationship between features and the target has changed (concept drift), causing the model's probability estimates to be misaligned with the true probabilities.
Why this is correct
Concept drift changes the conditional distribution P(Y|X), which directly affects calibration.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse covariate shift (feature distribution change) with prior probability shift (class imbalance change), and incorrectly attribute calibration degradation to feature drift rather than the direct effect of base rate change on probability estimates.
Trap categories for this question
Command / output trap
Overfitting would show poor test set performance, but the test set had good Brier score.
Detailed technical explanation
How to think about this question
In binary classification, the model's predicted probabilities are estimates of P(Y=1|X), which depend on the prior P(Y) via Bayes' theorem. When the base rate shifts from 5% to 0.5%, the model's logits or sigmoid outputs are no longer calibrated to the new prior, even if the conditional distributions P(X|Y) remain unchanged. This is a form of prior probability shift (a subtype of concept drift), and it can be corrected by recalibrating the model's output probabilities using Platt scaling or isotonic regression on production data.
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 relationship between features and the target has changed (concept drift), causing the model's probability estimates to be misaligned with the true probabilities. — The model's calibration degrades in production despite being well-calibrated on the test set, which had a 5% fraud rate, while production has a 0.5% fraud rate. This shift in class imbalance (prior probability shift) directly affects the model's probability estimates because the model's predicted probabilities are conditional on the training distribution. Option D is correct because concept drift—specifically a change in the base rate of fraud—causes the model's probability estimates to no longer reflect the true posterior probabilities in production, leading to a higher Brier score.
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.
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Last reviewed: Jun 11, 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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