PMLE Monitoring ML Solutions Practice Question
A data science team wants to monitor model quality by comparing predictions against ground truth labels. They have deployed a model on Vertex AI Endpoints and enable request/response logging to BigQuery. Which THREE actions should they take to set up model quality monitoring? (Choose 3)
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
✓
Create a scheduled query to compute metrics like accuracy and confusion matrix over time
To monitor model quality, the team needs to upload ground truth labels to BigQuery, join with prediction logs, compute metrics (e.g., accuracy, confusion matrix) over time, and optionally create dashboards.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a scheduled query to compute metrics like accuracy and confusion matrix over time
Why this is correct
Correct: Scheduled queries automate metric computation.
- ✓
Join prediction logs with ground truth labels on a common key (e.g., request ID)
Why this is correct
Correct: Joining allows comparing predictions to actuals.
- ✗
Configure Vertex AI Model Monitoring to detect prediction drift
Why it's wrong here
Prediction drift is different from model quality; ground truth is not needed for drift.
- ✗
Use Vertex AI Explainability to compute feature attributions
Why it's wrong here
Explainability is not needed for model quality monitoring.
- ✓
Upload ground truth labels to a BigQuery table
Why this is correct
Correct: Ground truth labels are essential for comparison.
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