Databricks-ML-Assoc Databricks Machine Learning Practice Question
A machine learning engineer is using Databricks Feature Store to create a feature table for a model that predicts customer churn. The feature table includes customer demographics and transaction history. The engineer wants to ensure that the model can access the latest feature values during online inference. What should the engineer do?
⚠ Common exam trap
A common mix-up: candidates confuse Delta Lake features like change data feed with online serving capabilities, which require a dedicated 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 API.
To serve features for online inference, the feature table must be published to an online store. Databricks Feature Store provides APIs to publish feature tables to low-latency databases, enabling the model to retrieve the latest feature values in real time. This is a key step when deploying models that require online features, 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.
- ✗
Enable change data feed on the feature table to capture updates.
Why it's wrong here
Change data feed (CDF) on Delta tables tracks row-level changes for downstream processing, but it does not provide a low-latency serving layer for online inference. CDF is useful for auditing or incremental updates, but it does not make features directly accessible to a model endpoint. Thus, it does not fulfill the need for online feature access.
- ✗
Set up a Databricks SQL warehouse to query the feature table.
Why it's wrong here
A SQL warehouse is optimized for analytical queries, not for low-latency online inference. While it can query the feature table, it introduces higher latency and is not designed for per-request feature lookups. The model endpoint would suffer from slow response times. Therefore, this approach is not suitable for online serving.
- ✓
Publish the feature table to an online store using the Databricks Feature Store API.
Why this is correct
Publishing the feature table to an online store makes the features available for low-latency lookups during online inference. The Databricks Feature Store supports online stores like Amazon DynamoDB or Azure Cosmos DB, enabling real-time serving of feature values. This is required to ensure the model can access the latest features at inference time.
- ✗
Use MLflow to log the feature table as a model artifact.
Why it's wrong here
Logging the feature table as an MLflow artifact would store a snapshot of the data, not provide a live serving endpoint. The model would not have access to the latest feature values at inference time; it would only see the static snapshot. This does not meet the requirement for real-time feature access, so it is incorrect.
About these practice questions
Courseiva writes every Databricks-ML-Assoc question from scratch — 319 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.