PMLE Monitoring ML Solutions Practice Question
An organization wants to collect ground truth labels for model quality monitoring and store them in BigQuery. They also want to compute and visualize a confusion matrix over time. Which TWO actions should they take? (Choose 2)
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
✓
Use Vertex AI Model Evaluation to compare predictions with ground truth and generate confusion matrix
Ground truth labels must be uploaded to BigQuery. Vertex AI Model Evaluation can then be used to compute metrics like confusion matrices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure Vertex AI Model Monitoring for prediction drift
Why it's wrong here
Model Monitoring does not use ground truth.
- ✗
Use Vertex AI Model Evaluation to run sliced evaluation
Why it's wrong here
Sliced evaluation is for fairness, not ground truth monitoring.
- ✗
Enable request/response logging to Cloud Logging
Why it's wrong here
Request/response logging captures predictions, but ground truth is separate.
- ✓
Use Vertex AI Model Evaluation to compare predictions with ground truth and generate confusion matrix
Why this is correct
Model Evaluation can compute confusion matrices over time from prediction and ground truth tables.
- ✓
Upload ground truth data to a BigQuery table
Why this is correct
Ground truth must be in BigQuery for comparison with predictions.
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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.