Quick Answer
The answer is to track changes in the relationship between inputs and outputs. This is correct because concept drift in production ML systems specifically refers to a shift in the underlying mapping between features and the target variable, which degrades model performance even when the input distribution remains unchanged. On the Google Professional Machine Learning Engineer exam, this question tests your ability to distinguish concept drift from data drift or covariate shift, a common trap where candidates mistakenly focus only on input distribution changes. In Vertex AI, you would monitor this via prediction residuals or performance metrics over time, not just feature statistics. A helpful memory tip: concept drift is about the “why” changing—the logic linking inputs to outputs—so always look at the prediction errors, not just the data coming in.
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 team deploys a model using Vertex AI and wants to monitor for concept drift. What should they track?
Number of prediction requests
Why wrong: Request count is operational.
Model prediction latency
Why wrong: Latency is an operational concern.
Changes in input data distribution
Why wrong: That is data drift, not concept drift.
Changes in the relationship between inputs and outputs
Concept drift is a change in the underlying function mapping inputs to outputs.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Changes in the relationship between inputs and outputs
Concept drift refers to a change in the underlying relationship between the input features and the target variable over time, which degrades model performance. In Vertex AI, monitoring this requires tracking the statistical relationship between inputs and outputs (e.g., via prediction residuals or model performance metrics), not just the input distribution alone. Option D correctly identifies this need, as concept drift is fundamentally about the input-output mapping shifting, even if the input distribution remains stable.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Number of prediction requests
Why it's wrong here
Request count is operational.
Model prediction latency
Why it's wrong here
Latency is an operational concern.
Changes in input data distribution
Why it's wrong here
That is data drift, not concept drift.
Changes in the relationship between inputs and outputs
Why this is correct
Concept drift is a change in the underlying function mapping inputs to outputs.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the distinction between data drift (input distribution changes) and concept drift (input-output relationship changes), and the trap here is that candidates confuse the two, picking Option C because they think monitoring input data is sufficient for detecting all model degradation.
Detailed technical explanation
Concept drift is often detected by tracking the model's prediction error over time using a sliding window or statistical tests (e.g., Page-Hinkley, ADWIN) on residuals. In Vertex AI, you can use Model Monitoring to set up alerts for feature attribution drift or prediction skew, which directly measure changes in the relationship between inputs and outputs. A real-world scenario: a fraud detection model may see stable transaction amounts (input distribution) but the fraud patterns change (e.g., new attack vectors), causing concept drift without data drift.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Changes in the relationship between inputs and outputs — Concept drift refers to a change in the underlying relationship between the input features and the target variable over time, which degrades model performance. In Vertex AI, monitoring this requires tracking the statistical relationship between inputs and outputs (e.g., via prediction residuals or model performance metrics), not just the input distribution alone. Option D correctly identifies this need, as concept drift is fundamentally about the input-output mapping shifting, even if the input distribution remains stable.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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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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