PMLE Monitoring ML Solutions Practice Question
An organization is deploying a loan approval model and wants to monitor for fairness across demographic subgroups. They have ground truth labels stored in BigQuery. How can they use Vertex AI to evaluate performance disparities between groups?
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
✓
Upload predictions and labels to Vertex AI Model Evaluation and specify slicing columns
Vertex AI Model Evaluation provides sliced evaluation, which computes metrics (e.g., accuracy, precision) per subgroup when slicing columns are specified. This enables detection of performance disparities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Upload predictions and labels to Vertex AI Model Evaluation and specify slicing columns
Why this is correct
Sliced evaluation in Vertex AI Model Evaluation can compare metrics across subgroups defined by columns.
- ✗
Query BigQuery and manually compute metrics per group, then visualize in Looker
Why it's wrong here
While possible, Vertex AI provides native functionality for this.
- ✗
Enable Vertex AI Model Monitoring with fairness detection
Why it's wrong here
Model Monitoring does not provide fairness evaluation; it focuses on drift.
- ✗
Use Vertex AI Explainable AI to get feature attributions per subgroup
Why it's wrong here
Explainable AI provides feature importance, not performance metrics per subgroup.
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Same concept, more angles
3 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- A.Cloud Data Loss Prevention (DLP)
- B.Vertex AI Explainable AI
- ✓ C.Vertex AI Model Evaluation
- D.Vertex AI Model Monitoring
Why C: Vertex AI Model Evaluation supports sliced evaluation, where metrics are computed for each subgroup defined by feature values (e.g., gender, race) in BigQuery. This helps identify performance disparities.
Variation 2. 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?
easy- A.Vertex AI Model Monitoring
- B.Vertex AI Prediction
- C.Vertex AI Explainability
- ✓ D.Vertex AI Model Evaluation
Why D: Vertex AI Model Evaluation supports sliced evaluation, allowing you to compute metrics per subgroup (e.g., by demographic) using data in BigQuery.
Variation 3. A machine learning engineer wants to monitor the fairness of a credit approval model across demographic subgroups. They have ground truth labels in BigQuery. Which approach should they use to evaluate performance disparities?
medium- ✓ A.Use Vertex AI Model Evaluation with sliced evaluation in BigQuery
- B.Use Vertex AI Explainability to compute feature attributions per subgroup
- C.Use Cloud Monitoring custom metrics to track predictions per subgroup
- D.Use Vertex AI Model Monitoring to detect skew in demographic features
Why A: Vertex AI Model Evaluation supports sliced evaluation, allowing comparison of metrics (like accuracy, precision) across subgroups defined by features like age, gender, etc.
JA
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.