PMLE Monitoring ML Solutions Practice Question
An organisation wants to monitor fairness of their loan approval model across demographic subgroups. They have predictions stored in BigQuery along with ground truth. Which GCP service can evaluate model performance for each subgroup and identify disparities?
⚠ Common exam trap
Test-takers frequently confuse model evaluation with model monitoring or explainability; candidates might think Model Monitoring handles fairness because it monitors models, but it only tracks drift, not performance disparities.
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 correct answer because it provides built-in functionality to compute performance metrics (e.g., accuracy, precision, recall, AUC) for specific slices of data, such as demographic subgroups. It allows you to evaluate fairness by comparing metrics across these subgroups to identify disparities. This service directly supports the requirement to monitor fairness by analyzing model performance on different segments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Data Loss Prevention (DLP)
Why it's wrong here
Cloud DLP discovers and de-identifies sensitive data such as personally identifiable information; it performs no model performance or fairness computation across demographic subgroups. It is tempting because fairness work involves sensitive attributes, but DLP would be the right choice for redacting or classifying that data, not evaluating disparities.
- ✗
Vertex AI Explainable AI
Why it's wrong here
Explainable AI attributes feature contributions to individual predictions; it does not compute subgroup metrics such as disparate impact or equal opportunity against ground truth. It is tempting because it surfaces model behaviour, and would suit explaining why one loan was approved, but fairness evaluation across demographic slices requires Vertex AI Model Monitoring or BigQuery ML fairness metrics.
- ✓
Vertex AI Model Evaluation
Why this is correct
Vertex AI Model Evaluation computes performance metrics and slices results by configured demographic columns, exposing fairness disparities across subgroups. This directly satisfies the stem's need to evaluate each subgroup using BigQuery predictions and ground truth labels.
- ✗
Vertex AI Model Monitoring
Why it's wrong here
Vertex AI Model Monitoring detects training-serving skew and drift on deployed endpoints; it does not compute per-subgroup fairness metrics from BigQuery prediction and label tables. It is tempting because it monitors model behaviour, but the required subgroup disparity analysis belongs to Vertex AI Model Evaluation or BigQuery ML fairness functions.
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JA
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.