PMLE Monitoring ML Solutions Practice Question
A team wants to collect ground truth labels for their model deployed on Vertex AI Endpoint to perform model quality monitoring. They have a process that generates actual outcomes within 24 hours of prediction. What is the recommended approach for storing these labels?
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 the ground truth labels to a BigQuery table with a schema that includes prediction timestamp and model version.
Vertex AI Model Monitoring for model quality requires ground truth data to be uploaded to BigQuery. Labels are stored in a BigQuery table with the prediction timestamp and model ID for comparison.
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 the ground truth labels to a BigQuery table with a schema that includes prediction timestamp and model version.
Why this is correct
Correct approach; BigQuery is required for ground truth storage.
- ✗
Use Vertex AI Experiments to log ground truth alongside training runs.
Why it's wrong here
Experiments are for tracking training runs, not production monitoring.
- ✗
Store the ground truth labels in Cloud Storage as CSV files and reference them in the monitoring config.
Why it's wrong here
Ground truth must be in BigQuery, not Cloud Storage.
- ✗
Insert ground truth labels directly into the Vertex AI Endpoint's log sink.
Why it's wrong here
Log sinks capture request/response logs, not ground truth.
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