PMLE Scaling Prototypes into ML Models Practice Question
A data engineer wants to compute feature aggregates over a large dataset stored in BigQuery and write the results to Vertex AI Feature Store. The pipeline must handle both batch and streaming data. Which Google Cloud service should they use?
⚠ Common exam trap
PMLE often tests the choice between batch-only and unified processing services, and candidates may pick BigQuery scheduled queries or Dataproc for streaming, missing Dataflow's unified capability.
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
✓
Cloud Dataflow with Apache Beam
Cloud Dataflow with Apache Beam is a unified stream and batch data processing service. It can read from BigQuery, compute aggregates, and write to Vertex AI Feature Store, handling both batch and streaming data with the same pipeline code. This makes it the ideal choice for the requirement.
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 scheduled queries
Why it's wrong here
Scheduled queries run only on a fixed cadence, so they cannot ingest streaming data continuously into Vertex AI Feature Store. It is tempting because they handle batch aggregation over BigQuery natively, and would be correct for purely periodic feature refreshes where near-real-time updates are not required.
- ✗
Cloud Functions triggered by Pub/Sub
Why it's wrong here
Cloud Functions imposes execution timeouts and stateless, per-event invocation, so it cannot aggregate large BigQuery datasets or maintain streaming state. It is tempting because Pub/Sub triggering handles event-driven work, and would be correct for lightweight per-message transformation or routing rather than feature computation.
- ✗
Cloud Dataproc with Spark
Why it's wrong here
Dataproc runs Spark jobs on Compute Engine or GKE clusters, so it cannot write natively into Vertex AI Feature Store or ingest streaming data without extra connectors. It suits migrating existing Hadoop or Spark workloads to Google Cloud, not building a managed batch-and-streaming feature pipeline directly into Feature Store.
- ✓
Cloud Dataflow with Apache Beam
Why this is correct
Cloud Dataflow with Apache Beam provides unified batch and streaming pipelines, reading from BigQuery and writing aggregates to Vertex AI Feature Store. This satisfies the stem's requirement to handle both data modes within one managed service.
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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
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.