Databricks-ML-Assoc ML Workflows Practice Question
A machine learning team is using Databricks Feature Store to serve features for a real-time model. They have a feature table that is updated daily with new data. To ensure the online store always has the latest feature values for low-latency inference, which approach should they take?
⚠ Common exam trap
The trap here is assuming that Delta Live Tables or materialized views automatically provide online serving; in reality, you must explicitly publish to an online store.
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
✓
Publish the feature table to an online store using the Databricks Feature Store UI or API, and schedule daily updates.
To serve features in real time with low latency, the feature table must be published to an online store, which is a key-value store optimized for fast lookups. Scheduling daily updates ensures the online store reflects the latest data. Other approaches like querying the offline table or using materialized views introduce latency and are not designed for online serving. Thus, publishing and scheduling updates is the correct strategy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Publish the feature table to an online store using the Databricks Feature Store UI or API, and schedule daily updates.
Why this is correct
Publishing to an online store (e.g., DynamoDB, Cosmos DB) enables low-latency lookups. Scheduling daily updates ensures the online store is refreshed with the latest feature values from the offline table. This is the standard pattern for real-time serving with Feature Store, as it synchronizes the online store with the offline source.
- ✗
Use the Feature Store's automatic online store synchronization by enabling a Delta Live Tables pipeline.
Why it's wrong here
While Delta Live Tables can process data, Feature Store does not automatically sync to an online store via DLT. You must explicitly publish to an online store. DLT alone does not provide the low-latency serving endpoint; it only manages data transformations. Thus, this approach would not meet the real-time requirement without additional publishing steps.
- ✗
Configure the model to query the offline feature table directly during inference, caching results in memory.
Why it's wrong here
Querying the offline table during inference introduces high latency and is not suitable for real-time serving. Offline tables are optimized for batch reads, not low-latency point lookups. Caching in memory might help but does not solve the fundamental issue of slow access to the offline store. This approach would degrade inference performance.
- ✗
Create a materialized view of the feature table in Databricks SQL and use that for online serving.
Why it's wrong here
Materialized views in Databricks SQL are designed for analytical queries, not low-latency online serving. They do not provide the sub-millisecond latency required for real-time inference. Additionally, they are not integrated with Feature Store's online serving capabilities. This method would not meet the performance needs of a real-time model.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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