PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
You are collaborating on a Vertex AI Feature Store implementation. A data engineer updates a feature's values in the offline store, but the online store still serves the old values for several hours. The online store is configured with a feature value TTL of 24 hours and uses batch ingestion. What is the most likely cause of the stale online values?
⚠ Common exam trap
The trap here is assuming that updating the offline store automatically propagates to the online store, or that TTL controls synchronization rather than validity.
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
✓
The feature values are ingested into the offline store only, and the online store is not being updated because batch ingestion does not automatically sync to the online store unless a separate ingestion job is run.
Vertex AI Feature Store maintains separate offline and online stores. Batch ingestion updates the offline store, but the online store requires its own ingestion job to sync values. Without running that job, the online store continues to serve the previous values. The TTL affects validity, not propagation, so the missing sync job is the most likely cause.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The online store is configured to read from the offline store at query time, and the delay is due to eventual consistency between the two stores.
Why it's wrong here
Vertex AI Feature Store online serving reads from the online store, not directly from the offline store at query time. There is no automatic read-through to the offline store. The online store must be populated via ingestion. Eventual consistency is not the mechanism; the lack of a sync job is the issue.
- ✓
The feature values are ingested into the offline store only, and the online store is not being updated because batch ingestion does not automatically sync to the online store unless a separate ingestion job is run.
Why this is correct
In Vertex AI Feature Store, offline and online stores are separate. Batch ingestion writes to the offline store, and you must explicitly run an ingestion job to update the online store, or use streaming ingestion for real-time updates. If the data engineer only updated the offline store, the online store will not reflect the changes until a sync job is executed.
- ✗
The online store's feature value TTL is set too high, causing it to serve cached values until the TTL expires.
Why it's wrong here
The TTL determines how long a feature value is considered valid in the online store, but it does not control the propagation of new values from the offline store. A high TTL means stale values may be served longer if they are not updated, but the root cause here is the ingestion method. Batch ingestion does not immediately update the online store; reducing TTL would not fix the delay.
- ✗
The feature's online store TTL has expired, causing the online store to fall back to the offline store for the latest values.
Why it's wrong here
When a feature value TTL expires in the online store, the online store does not fall back to the offline store; it simply returns no value or a default. The online store does not query the offline store dynamically. Therefore, an expired TTL would not cause stale values to be served; it would cause missing values.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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