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PMLE Practice Question: A team deploys a model using Vertex AI and wants…
A team deploys a model using Vertex AI and wants to monitor for concept drift. What should they track?
⚠ Common exam trap
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.
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
✓
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.
Answer analysis
Option-by-option breakdown
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 volume is an operational throughput metric and reveals nothing about whether the model's learned relationship still holds. Concept drift demands monitoring of prediction quality against ground truth or feature-target dependencies. Request counting suits capacity planning and traffic analysis, not drift detection.
- ✗
Model prediction latency
Why it's wrong here
Prediction latency measures serving performance, not the statistical relationship between inputs and outcomes. Concept drift requires tracking changes in the mapping the model learned, typically via label or quality metrics. Latency monitoring is the correct focus when the concern is infrastructure responsiveness or SLA breaches.
- ✗
Changes in input data distribution
Why it's wrong here
Input distribution shifts describe data drift, not concept drift. Concept drift concerns the relationship between features and the target changing, so Vertex AI monitoring should track prediction quality or label-based metrics. Data distribution tracking is right when the requirement is detecting covariate shift instead.
- ✓
Changes in the relationship between inputs and outputs
Why this is correct
Concept drift is defined by a change in the statistical relationship between input features and target outputs, so tracking that mapping is the only signal that captures it. Feature distribution shifts alone indicate data drift, not concept drift.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.