PMLE Monitoring ML Solutions Practice Question
An MLOps engineer needs to collect ground truth labels for a deployed classification model to compare predictions against actuals. Where should the engineer store the ground truth data to enable Vertex AI model quality monitoring?
⚠ Common exam trap
PMLE often tests the assumption that any Google database can serve as a monitoring data source, but model quality monitoring specifically requires BigQuery because the service performs SQL joins between predictions and ground truth.
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
✓
BigQuery
Vertex AI Model Monitoring for model quality requires ground truth labels to be stored in BigQuery. The monitoring job compares the model's online predictions (logged to BigQuery via prediction logging) against the actual observed labels, which must also reside in BigQuery so the service can join them on a shared key column. BigQuery is the only supported sink for ground truth data 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.
- ✓
BigQuery
Why this is correct
Vertex AI model quality monitoring reads ground truth labels from a BigQuery table, joining them against logged predictions to compute accuracy, precision and recall. Storing labels in BigQuery satisfies this requirement because the monitoring pipeline expects that source, unlike Cloud Storage objects or local files.
- ✗
Firestore
Why it's wrong here
Vertex AI model quality monitoring expects ground truth in a Cloud Storage bucket or BigQuery table, not Firestore. Firestore is a document database for application state and real-time sync, so it cannot serve as the monitoring input source. It is tempting as a managed store, but the wrong ingestion format.
- ✗
Cloud Spanner
Why it's wrong here
Cloud Spanner is a globally distributed relational database for transactional workloads, and Vertex AI model quality monitoring does not ingest ground truth from it. Ground truth must land in Cloud Storage or BigQuery. Spanner is tempting for its scale, but it is not a supported monitoring data source.
- ✗
Cloud Storage
Why it's wrong here
Vertex AI model quality monitoring reads ground truth labels from a BigQuery table joined to the prediction request/response logs, not from object storage. Cloud Storage holds unstructured artefacts such as model files and batch inputs, so it would be correct for staging training data or exporting predictions, but it cannot serve as the labelled reference source for skew and drift computation.
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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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