PMLE Scaling Prototypes into ML Models Practice Question

An ML team is building a feature pipeline with Dataflow that reads from BigQuery, computes features, and writes to Vertex AI Feature Store. They need to ensure that features are available for both training and serving with low latency. Which Feature Store option should they use?

⚠ Common exam trap

The trap is assuming that storing features in BigQuery or Cloud SQL is sufficient for low-latency serving; candidates may overlook that Vertex AI Feature Store's online serving is specifically designed for millisecond-latency access and integration with training pipelines.

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

✓

Create a featurestore with online serving enabled

To ensure features are available for both training and serving with low latency, the featurestore must have online serving enabled. Vertex AI Feature Store provides both online serving (low-latency reads for real-time predictions) and offline serving (batch reads for training). Enabling online serving allows the same feature values to be served at low latency during prediction, ensuring consistency between training and serving.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Create a featurestore with only offline serving
Why it's wrong here
An offline-only featurestore cannot serve features at low latency; online serving must be enabled for real-time prediction lookups. It is tempting because offline serving supports training and batch scoring cheaply, and would be the correct choice when features are needed only for training rather than online inference.
✗
Store features directly in BigQuery
Why it's wrong here
BigQuery stores training data but serves online predictions with too much latency for real-time lookups, and it is not a Vertex AI Feature Store option. It is tempting because BigQuery already holds the source data and suits offline training and batch scoring, which would be correct when only offline feature retrieval is required.
✗
Use Cloud SQL as a feature store
Why it's wrong here
Cloud SQL is a relational database, not a Vertex AI Feature Store; it lacks the managed online serving and feature registry that low-latency serving requires. It is tempting because Cloud SQL offers low-latency reads for application data, and would be correct when storing operational records rather than ML features.
✓
Create a featurestore with online serving enabled
Why this is correct
Enabling online serving on the featurestore provisions a low-latency endpoint that serves features in real time, while the same store retains the data for training retrieval. This dual capability satisfies both training and serving requirements from one featurestore.

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PMLE Scaling Prototypes into ML Models Practice Question

Why each option matters

Option-by-option breakdown

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