Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist is using Databricks Feature Store to create a feature table for a recommendation model. They want to ensure that the same feature computation logic is used both during training and at inference time to avoid training-serving skew. Which Feature Store capability directly addresses this requirement?
⚠ Common exam trap
Many candidates confuse schema validation or point-in-time correctness with the shared computation logic that actually prevents training-serving skew.
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
✓
Feature Store allows features to be computed once and reused across training and batch or online serving through a unified feature computation function.
Databricks Feature Store reduces training-serving skew by letting you define a feature computation function that is used both to create the feature table for training and to compute features at inference time. This unified logic ensures that the model sees features derived from the same transformations in both phases.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Feature Store allows features to be computed once and reused across training and batch or online serving through a unified feature computation function.
Why this is correct
By defining a feature computation function and using it to populate the feature table, the same transformation logic is applied when materializing features for training and when serving features online. This shared logic is the primary mechanism Feature Store provides to prevent training-serving skew, because the model consumes identically computed features in both contexts.
- ✗
Feature Store provides a point-in-time lookup that ensures training data reflects the state of features at the time of each label event.
Why it's wrong here
Point-in-time lookups prevent label leakage by aligning feature values with event timestamps, but they do not ensure that the same transformation code runs at inference. This capability addresses temporal correctness rather than code consistency between training and serving pipelines.
- ✗
Feature Store enforces schema validation on feature tables, rejecting any inference request that does not match the training schema.
Why it's wrong here
Schema validation ensures data types and column names are consistent, which is helpful, but it does not guarantee that the values were computed using the same logic. A request could match the schema yet contain features derived from a different transformation, reintroducing skew. Schema enforcement is a guardrail, not the core mechanism for avoiding training-serving skew.
- ✗
Feature Store automatically versions feature tables and tracks lineage from source data to model.
Why it's wrong here
Versioning and lineage help with reproducibility and auditing, but they do not guarantee that the same transformation code is applied at inference. Training-serving skew arises from inconsistent logic, not from missing version history. While lineage is valuable, it does not directly enforce identical computation between training and 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-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.