Databricks-ML-Assoc ML Workflows Practice Question
A data scientist is preparing a feature table in Databricks Feature Store. To ensure the feature table can be used for online inference with low latency, which step is mandatory?
⚠ Common exam trap
Students often assume registering a model in the Workspace automatically provisions online feature retrieval, missing the explicit sync requirement 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
✓
Use the publish_table method to sync features to an online store configured in the Feature Store.
To enable online serving, the feature table must be published to a supported online store like Amazon DynamoDB, Azure Cosmos DB, or Google Cloud Bigtable. This process decouples the feature retrieval from the complex logic of feature engineering pipelines, allowing real-time models to fetch pre-computed features in milliseconds rather than recalculating them during the inference request, which is critical for high-throughput production ML applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Register the feature table in the Unity Catalog without any additional configuration.
Why it's wrong here
Registering in Unity Catalog manages metadata and governance but does not automatically provision or sync data to the specialized low-latency backend required for sub-millisecond online lookups. You must explicitly invoke the publish operation to move the data to an online database infrastructure.
- ✗
Call the write_batch method to export features to an S3 bucket for the model to read at runtime.
Why it's wrong here
Exporting to S3 is suitable for offline batch processing or training but does not provide the random access performance required for online inference. Online inference requires a NoSQL-style database backend capable of performing primary-key lookups in real-time, which S3 cannot provide.
- ✓
Use the publish_table method to sync features to an online store configured in the Feature Store.
Why this is correct
The publish_table method is the dedicated API function in Databricks Feature Store designed to replicate feature data from the offline store to an online store. This ensures the features are available for fast key-value lookups, fulfilling the latency requirements necessary for real-time model scoring requests.
- ✗
Include the feature table in a Databricks Job that runs every minute to keep the data fresh.
Why it's wrong here
Running a batch job every minute is inefficient and introduces high latency variability. Online inference stores are designed to be queried directly by the model server, and data should be pushed to them via the publish mechanism rather than relying on frequent batch job triggers.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
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.