Databricks-ML-Assoc ML Workflows Practice Question
You are using Databricks Feature Store to create a feature table that will be used for both batch training and online inference. The feature table must be refreshed daily with new data, and the online store must serve the latest feature values within minutes of the refresh. Which configuration should you use?
⚠ Common exam trap
The trap here is assuming that Model Serving can read directly from the offline Delta table, when online inference requires a feature table published 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
✓
Create a feature table with online=True and use a Databricks Job to run a daily write that updates both the offline Delta table and the online store.
A feature table created with online=True is backed by both an offline Delta table and an online store. Writing to it via FeatureStoreClient.write_table publishes the new feature values to both stores. Running this write in a daily Databricks Job refreshes the online store with the latest data, enabling low-latency serving within minutes. Other options either omit the online store, use unsupported manual exports, or fail to meet freshness requirements.
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 feature table with online=False, and configure Model Serving to read from the offline table at inference time.
Why it's wrong here
online=False means no online store is created or updated. Model Serving cannot directly query the offline Delta table for low-latency feature lookup; it requires an online store for real-time serving. This configuration would fail to meet the requirement of serving the latest features within minutes, as the offline table is not designed for per-request lookups.
- ✗
Create a standard Delta table and manually export it to a key-value store each day using a notebook.
Why it's wrong here
A standard Delta table lacks Feature Store metadata and lineage, so it cannot be used with FeatureStoreClient for training or serving. Manually exporting to a key-value store bypasses the Feature Store's integration, losing point-in-time correctness and automatic online publishing. This approach is error-prone and does not provide the managed online store behavior required for low-latency serving.
- ✓
Create a feature table with online=True and use a Databricks Job to run a daily write that updates both the offline Delta table and the online store.
Why this is correct
Setting online=True when creating the feature table enables an online store (such as DynamoDB or SQL) alongside the offline Delta table. A daily job that writes to the feature table via FeatureStoreClient.write_table updates both stores, and the online store is refreshed with the new values. This meets the requirement for daily refresh and near-real-time online serving, as Databricks Feature Store publishes to the online store during the write.
- ✗
Create a feature table with online=True, but only write to it weekly to reduce costs.
Why it's wrong here
While online=True enables the online store, a weekly write schedule does not satisfy the daily refresh requirement. The online store would contain stale features for up to a week, violating the need to serve the latest values within minutes of a daily refresh. The write frequency must match the required freshness, so a daily job is necessary.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-ML-Assoc practice question is part of Courseiva's free Databricks 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 Databricks-ML-Assoc exam.