Databricks-ML-Pro ML Ops Practice Question
A team is using Databricks Feature Store to manage features for a real-time model served via Databricks Model Serving. They need to ensure that the online feature values used at inference time are consistent with the training data. Which TWO practices should they implement? (Choose two.)
⚠ Common exam trap
The trap here is thinking that logging the model with the training set specification alone guarantees consistency, when the online store must also be updated with the same pipeline.
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 point-in-time lookups when creating the training dataset to avoid leaking future data.
Consistency between online and offline features is achieved by using the same pipeline to publish to both stores and by using point-in-time lookups during training. These practices ensure that the transformations and temporal alignment match between training and inference. Manual copying or direct Delta queries do not provide the required consistency or 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.
- ✗
Log the model with the feature store's training set specification to enable automatic feature lookup at serving time.
Why it's wrong here
While logging with the training set specification is a good practice for feature lookup, it alone does not ensure consistency if the online store is not updated correctly. The question asks for practices that ensure consistency; this option is about enabling lookup, not about maintaining data parity. It is part of the solution but not sufficient by itself, and the stem asks for two practices, with A and B being the core ones.
- ✗
Store all features in a single Delta table and query it directly from the model serving endpoint.
Why it's wrong here
Querying a Delta table directly from a serving endpoint is not supported for low-latency lookups and would introduce high latency. Feature Store online stores are optimized for real-time access. This approach would also bypass the consistency mechanisms of Feature Store, risking skew and performance issues.
- ✓
Use point-in-time lookups when creating the training dataset to avoid leaking future data.
Why this is correct
Point-in-time lookups ensure that training examples only use feature values available at the time of the label event, mimicking real-time inference conditions. This prevents data leakage and aligns training with serving. Databricks Feature Store provides time-series lookups for this purpose, which is essential for temporal consistency.
- ✓
Publish feature tables to an online store that is updated with the same pipeline that writes to the offline store.
Why this is correct
Publishing to an online store using the same pipeline ensures that online and offline features share the same transformation logic and data sources. This consistency prevents training-serving skew. Databricks Feature Store supports online stores like DynamoDB or Azure Cosmos DB, and the publish operation should be part of the same workflow that updates the offline table.
- ✗
Manually copy feature values from the offline store to the online store on a daily basis using a notebook.
Why it's wrong here
Manual copying is error-prone and can introduce delays or mismatches. It does not guarantee that the same transformations are applied, leading to training-serving skew. Automated pipelines are required to maintain consistency. This practice is not recommended and would likely cause discrepancies between online and offline features.
About these practice questions
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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-Pro 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-Pro exam.