PMLE Serving and Scaling Models Practice Question
Which TWO of the following can be used as input sources for Vertex AI batch prediction jobs? (Choose 2)
⚠ Common exam trap
Many candidates assume any Google Cloud database (like Firestore, Cloud SQL, or Spanner) can serve as a direct input source for batch predictions, but Vertex AI batch prediction only supports BigQuery and Cloud Storage as input sources, requiring data to be exported or staged in those services first.
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
Option C (BigQuery) is correct because Vertex AI batch prediction jobs natively accept a BigQuery table as the input source, specified via the bigQuerySource field in the BatchPredictionJob, allowing you to run predictions directly against tabular data stored in BigQuery. Option E (Cloud Storage) is correct because Vertex AI batch prediction jobs also accept input files (such as JSONL, CSV, or TFRecord) stored in a Cloud Storage bucket, specified via the gcsSource field with a URI like gs://bucket/file.jsonl. Options A (Cloud Firestore), B (Cloud SQL), and D (Cloud Spanner) are not valid input sources for Vertex AI batch prediction; these are operational databases that are not directly supported as batch prediction inputs, so you would need to export their data to BigQuery or Cloud Storage first.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Firestore
Why it's wrong here
Vertex AI batch prediction accepts Cloud Storage and BigQuery inputs; Firestore is a document database with no batch prediction input path. It is tempting because Firestore stores application data that could feed predictions, and it would be correct as a source only after exporting to Cloud Storage or BigQuery.
- ✗
Cloud SQL
Why it's wrong here
Vertex AI batch prediction supports Cloud Storage and BigQuery as input sources; Cloud SQL is a managed relational database with no direct batch input integration. It is tempting because Cloud SQL holds operational data, and it would be correct as a source only after exporting to Cloud Storage or BigQuery.
- ✓
BigQuery
Why this is correct
BigQuery is a supported input source for Vertex AI batch prediction jobs, letting you point the job directly at a table or query. This avoids exporting data to Cloud Storage first, satisfying the question's requirement for valid batch prediction input sources.
- ✗
Cloud Spanner
Why it's wrong here
Vertex AI batch prediction reads input from Cloud Storage or BigQuery only; Spanner is an OLTP database with no batch input integration. It is tempting because Spanner holds the training data, and it would be correct as a source when exporting that data to BigQuery or Cloud Storage first.
- ✓
Cloud Storage
Why this is correct
Cloud Storage is a supported input source for Vertex AI batch prediction jobs, accepting files such as JSON Lines, CSV, or TFRecord. It satisfies the question's requirement for valid batch prediction input sources, alongside BigQuery, without any intermediate conversion service.
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