Databricks-ML-Assoc Databricks Machine Learning Practice Question
A machine learning team is using Databricks Feature Store to serve features for online inference. They need to ensure that the online store remains consistent with the offline store and supports low-latency lookups. Which two practices should they follow? (Choose two.)
⚠ Common exam trap
The trap here is assuming that the online store automatically stays in sync without any explicit publishing or scheduling, but it requires deliberate synchronization steps.
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
✓
Schedule regular updates to the online store to reflect changes in the offline feature table.
Publishing feature tables to the online store and scheduling regular updates are essential to keep online and offline stores consistent. The publish_table API automates the sync, and scheduled jobs ensure freshness. Manual copying, using the online store as primary, or disabling sync would compromise consistency and performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually copy feature values from the offline store to the online store using a notebook.
Why it's wrong here
Manual copying is error-prone, not scalable, and does not guarantee consistency. It also lacks the automation and monitoring provided by the publish_table API. This approach would require significant effort to maintain and is not recommended for production use, as it can introduce latency and human error.
- ✓
Schedule regular updates to the online store to reflect changes in the offline feature table.
Why this is correct
Scheduling regular updates via jobs ensures that the online store stays in sync with the offline store as new data arrives. Without periodic refreshes, the online store would become stale, leading to inconsistent predictions. This practice is critical for maintaining feature freshness and consistency in production systems.
- ✗
Disable automatic synchronization to reduce overhead.
Why it's wrong here
Disabling synchronization would cause the online store to diverge from the offline store, leading to stale features and incorrect predictions. Automatic synchronization mechanisms are designed to maintain consistency with minimal overhead. Disabling them defeats the purpose of the Feature Store and is not a recommended practice.
- ✓
Publish feature tables to the online store using the publish_table API or the FeatureStoreClient.
Why this is correct
The publish_table method (or FeatureStoreClient.publish_table) syncs the offline feature table to the online store, ensuring consistency. It handles the data transfer and updates the online store with the latest features. This is the standard way to make features available for real-time serving and is essential for maintaining parity between offline and online stores.
- ✗
Use the online store as the primary source of truth for feature engineering.
Why it's wrong here
The online store is optimized for low-latency reads, not for feature engineering or storage of large datasets. The offline store (Delta Lake) is the primary source of truth. Using the online store as primary would limit analytical capabilities and could lead to data duplication and inconsistency.
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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.