PMLE Monitoring ML Solutions Practice Question
A company wants to monitor fairness of a model by evaluating performance metrics across demographic subgroups. They have ground truth labels stored in BigQuery. Which Vertex AI service should they use?
⚠ Common exam trap
The trap is conflating Model Monitoring with Model Evaluation — candidates assume 'monitoring fairness' means Model Monitoring, but fairness against ground truth requires Model Evaluation's labeled metrics.
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
✓
Vertex AI Model Evaluation
Vertex AI Model Evaluation is the service designed to assess model performance using ground truth labels, including slicing metrics across demographic subgroups to detect fairness issues. It computes metrics like precision, recall, and AUC per slice, directly supporting bias and fairness analysis. This makes it the correct choice when labels are available in BigQuery.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI Model Monitoring
Why it's wrong here
Model Monitoring detects training-serving skew and drift on deployed endpoints; it does not compute subgroup fairness metrics against BigQuery ground truth labels. Fairness evaluation across demographic slices requires Vertex AI Model Evaluation, which would be correct for this labelled, offline analysis.
- ✗
Vertex AI Prediction
Why it's wrong here
Prediction serves online and batch inference requests; it returns model outputs but computes no subgroup metrics and cannot join BigQuery ground-truth labels for fairness analysis. It is tempting because it is the service hosting the model being audited, yet evaluation against labels belongs to Vertex AI Model Evaluation.
- ✗
Vertex AI Explainability
Why it's wrong here
Explainability attributes feature contributions to individual predictions; it does not compute accuracy, precision or recall per demographic subgroup against BigQuery labels. It is tempting because it also examines model behaviour, but its output is feature attribution, not fairness metrics, so it cannot satisfy the subgroup-performance requirement.
- ✓
Vertex AI Model Evaluation
Why this is correct
Vertex AI Model Evaluation computes performance metrics, including fairness and bias slices, by comparing model predictions against ground truth labels. Pointing it at the BigQuery label data lets the team evaluate metrics across demographic subgroups, satisfying the fairness monitoring requirement.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.