The correct answer is that concept drift is occurring, which is not captured by drift or skew detection. This is because concept drift specifically involves a change in the statistical relationship between the input features and the target variable, while data drift monitors shifts in the input feature distributions themselves. In this scenario, the absence of data drift alerts confirms the input data remains stable, yet the model’s performance has degraded—a classic sign that the underlying mapping from features to labels has shifted. On the Google Professional Machine Learning Engineer exam, this question tests your ability to distinguish between data drift and concept drift, a common trap where candidates assume no alerts mean no drift. The key insight is that concept drift detection requires monitoring model performance metrics like accuracy or F1-score, not feature distributions. Memory tip: think of it as “the target moved, not the features”—concept drift is a change in the “why,” not the “what.”
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Concept drift is occurring, which is not captured by drift or skew detection
Concept drift occurs when the statistical properties of the target variable change over time, causing model performance to degrade even when the input data distribution remains stable. Drift detection (e.g., data drift or skew) monitors changes in feature distributions, not the relationship between features and the target. Since no drift alerts were triggered, the input data appears unchanged, but the model's predictive relationship has shifted — this is classic concept drift, which requires performance monitoring (e.g., accuracy, F1-score) rather than drift or skew detection.
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.
✗
Feature attribution monitoring is causing too many false positives
Why it's wrong here
Feature attribution does not suppress drift alerts.
✗
Drift threshold for income is too high
Why it's wrong here
Even if true, it only affects income drift alerts, not concept drift.
✗
Skew thresholds are not configured for categorical features
Why it's wrong here
The config shows skew thresholds for age and income, so categoricals are not the issue.
✓
Concept drift is occurring, which is not captured by drift or skew detection
Why this is correct
Concept drift affects the model's predictive relationship, not input distributions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between data drift (input feature changes) and concept drift (target relationship changes), trapping candidates who assume that no drift alerts mean the model is healthy, when in fact performance degradation can occur without any feature distribution shift.
Trap categories for this question
Command / output trap
The config shows skew thresholds for age and income, so categoricals are not the issue.
Detailed technical explanation
How to think about this question
Concept drift is often detected by tracking model performance metrics over time (e.g., using a sliding window of predictions vs. actuals) or by monitoring the distribution of residuals. In production ML systems, tools like Amazon SageMaker Model Monitor or Azure ML can be configured to alert on concept drift via a performance baseline, but this is separate from data drift/skew checks that compare input feature distributions (e.g., using Jensen-Shannon divergence or Chi-square tests). A real-world example: a credit scoring model may see no change in applicant income or age (no data drift) but the economic environment shifts, making past default patterns invalid — this is concept drift.
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.
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: Concept drift is occurring, which is not captured by drift or skew detection — Concept drift occurs when the statistical properties of the target variable change over time, causing model performance to degrade even when the input data distribution remains stable. Drift detection (e.g., data drift or skew) monitors changes in feature distributions, not the relationship between features and the target. Since no drift alerts were triggered, the input data appears unchanged, but the model's predictive relationship has shifted — this is classic concept drift, which requires performance monitoring (e.g., accuracy, F1-score) rather than drift or skew detection.
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.
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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