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?
⚠ Common exam trap
PMLE often tests the assumption that any storage (GCS, Experiments) can hold ground truth — the trap is forgetting that Vertex AI model quality monitoring only reads ground truth from BigQuery with a specific schema.
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 expects ground truth labels to be stored in a BigQuery table whose schema includes the prediction timestamp and model version (plus the prediction output and the actual label). This lets the service join ground truth back to logged predictions within the monitoring window and compute quality metrics like accuracy, precision, and recall. BigQuery is the only supported sink for ground truth in Vertex AI model quality monitoring.
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
BigQuery stores ground truth with prediction timestamp and model version columns, letting Vertex AI join labels to logged predictions for skew and drift analysis. The 24-hour outcome delay fits batch upload, and the schema keys each label to the exact prediction it evaluates.
- ✗
Use Vertex AI Experiments to log ground truth alongside training runs.
Why it's wrong here
Vertex AI Experiments tracks parameters, metrics and artefacts for training runs, not production ground truth for deployed endpoint monitoring. Logging labels there leaves the monitoring job with no BigQuery or Cloud Storage source to join against. Experiments is correct when comparing training iterations, not for serving-time quality evaluation.
- ✗
Store the ground truth labels in Cloud Storage as CSV files and reference them in the monitoring config.
Why it's wrong here
Vertex AI model monitoring reads ground truth labels from a BigQuery table or a Cloud Storage path, but CSV files in Cloud Storage lack the indexed, queryable structure needed for scheduled label ingestion and metric computation. Cloud Storage suits bulk batch imports, not the continuous label joining that quality monitoring requires.
- ✗
Insert ground truth labels directly into the Vertex AI Endpoint's log sink.
Why it's wrong here
Endpoints emit prediction logs to Cloud Logging; they have no writable sink for ground truth labels. The monitoring service ingests labels from BigQuery or Cloud Storage, so writing into the endpoint's log stream is not a supported ingestion path. Log sinks are for exporting prediction data, not receiving outcomes.
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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
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