Databricks-ML-Pro Model Development Practice Question
A machine learning engineer is building a feature engineering pipeline in Databricks using Feature Store. They need to ensure that the same feature computation logic is used for both training and batch scoring, and that features are automatically refreshed. Which approach should they take?
⚠ Common exam trap
The trap here is assuming that any scheduled job or MLflow artifact can replace Feature Store's feature function and materialization for consistent training and scoring.
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
✓
Define a feature computation function using the Feature Store client, register it, and create a Feature Store table with a scheduled refresh.
Defining a feature computation function and registering it with Feature Store ensures that training and scoring use identical logic. Creating a Feature Store table with scheduled refresh automates feature updates. This combination provides consistency, reproducibility, and freshness. The other options rely on manual steps or lack the necessary integration with Feature Store, so they do not guarantee consistent feature computation and automatic refresh.
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 Store table using a PySpark UDF that reads from a Delta table and schedule a job to refresh it.
Why it's wrong here
While a PySpark UDF can compute features, using it directly in a Feature Store table does not ensure the same logic is used at inference time unless the UDF is registered as a feature function. Scheduling a job to refresh the table is good, but without a feature function, training and scoring may diverge. Feature Store requires defining feature computation as a function to guarantee consistency and automatic refresh.
- ✗
Write a Python function that computes features and call it manually in both training and scoring notebooks.
Why it's wrong here
Manually calling a Python function in separate notebooks does not guarantee consistency and does not provide automatic refresh. It is error-prone because changes to the function must be propagated manually to all notebooks. Feature Store is designed to centralize feature computation and serve features consistently. This approach lacks versioning, lineage, and automatic materialization, so it fails to meet the requirement for consistent training and scoring features.
- ✗
Use MLflow to log the feature engineering code as an artifact and manually apply it during scoring.
Why it's wrong here
Logging code as an MLflow artifact provides versioning but does not automate feature computation or refresh. Manually applying the code during scoring is error-prone and does not guarantee consistency. Feature Store is purpose-built for this use case, offering feature functions and automatic materialization. MLflow alone cannot serve features or refresh them, so this approach fails to meet the requirements.
- ✓
Define a feature computation function using the Feature Store client, register it, and create a Feature Store table with a scheduled refresh.
Why this is correct
Defining a feature computation function with the Feature Store client and registering it ensures that the same logic is used for training and scoring. Creating a Feature Store table with a scheduled refresh automatically updates features. This approach provides consistency, versioning, and lineage. It directly satisfies the need for identical feature computation and automatic refresh, making it the correct solution for the engineer's pipeline.
About these practice questions
One of 300 original Databricks-ML-Pro practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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-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.