Databricks-ML-Pro ML Ops Practice Question
You are migrating a legacy ML pipeline to Databricks. You need to ensure that the feature engineering logic used during training is identical to the logic used during real-time inference. What is the recommended approach?
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 Databricks Feature Store to define and log features.
Consistency between training and inference (the 'training-serving skew') is a primary cause of model failure. The Databricks Feature Store acts as the single source of truth for feature definitions. By using it, you ensure that the same transformations are applied during both training and inference, eliminating discrepancies that arise from re-implementing logic in different languages or frameworks, which is critical for model reliability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Copy the feature transformation code into both the training notebook and the inference service.
Why it's wrong here
Duplicating code is highly error-prone and leads to training-serving skew. If the code is updated in one place but not the other, the model will receive incorrectly formatted features, leading to poor predictions. Centralizing transformation logic is essential for maintaining consistency and reducing the risk of silent model degradation.
- ✓
Use the Databricks Feature Store to define and log features.
Why this is correct
The Feature Store enables the packaging of features with their associated transformation logic. When you use the Feature Store for training, it automatically logs the transformations. During inference, you can then retrieve the features using the same ID, ensuring that the exact same logic is applied to the input data.
- ✗
Store the features in a Parquet file and load it during both training and inference.
Why it's wrong here
Storing features in static files does not capture the transformation logic itself. It only captures the output. If the inference service receives raw data that needs transformation, the static file approach fails to provide the required preprocessing, leading to a mismatch between the training data features and the live inference features.
- ✗
Implement the transformation logic as a SQL view in the Hive Metastore.
Why it's wrong here
SQL views are useful for data retrieval but do not encapsulate complex feature engineering logic (like window functions or custom Python transformations) in a way that is easily reusable for real-time inference. The Feature Store provides a superior abstraction that manages both the data and the logic, ensuring consistent serving.
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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.