Databricks-ML-Pro Model Development Practice Question
A machine learning team is using Databricks Feature Store to manage features for their models. They want to ensure that the features used during training are consistent with those served in production. Which TWO practices should they follow? (Choose two.)
⚠ Common exam trap
The trap here is thinking that any method that retrieves features, such as batch scoring, is sufficient for ensuring training-serving consistency, when the key is to use the training set creation and model logging with feature lookups.
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
✓
Log the model with `FeatureStoreClient.log_model`, providing the feature lookups used during training.
To ensure consistency between training and serving with Databricks Feature Store, teams should use `create_training_set` to build training data from feature lookups and log models with `log_model` including those lookups. These practices embed the feature retrieval logic into the model, enabling automatic and consistent feature serving. Manual copying or disabling online publishing would introduce inconsistencies and are not recommended.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use `FeatureStoreClient.score_batch` to score data in batch mode, which automatically handles feature retrieval.
Why it's wrong here
While `score_batch` is useful for batch scoring and does retrieve features automatically, it is not a practice for ensuring training-serving consistency. It is a scoring method, not a development practice. The question asks for practices to follow during development to ensure consistency, which are creating the training set and logging the model with feature lookups.
- ✗
Manually copy feature values from the offline store to the online store before each scoring request.
Why it's wrong here
Manually copying feature values is error-prone and not scalable. Databricks Feature Store automatically publishes features to the online store when you write to a feature table, and it handles synchronization. Manual copying could lead to stale or inconsistent data and is not a recommended practice. It also does not ensure that the model uses the correct features at serving time.
- ✓
Log the model with `FeatureStoreClient.log_model`, providing the feature lookups used during training.
Why this is correct
Logging the model with `log_model` and including the feature lookups packages the model with metadata about the features it requires. When the model is served, Databricks can automatically retrieve the necessary features from the online store, ensuring consistency. This is a key practice to maintain parity between training and serving, as it links the model to the exact feature definitions.
- ✓
Use `FeatureStoreClient.create_training_set` to build the training dataset, specifying the feature lookups.
Why this is correct
`create_training_set` is the recommended method to construct training data from features in the Feature Store. It ensures that the correct feature values are retrieved based on the provided lookups and point-in-time correctness. This practice guarantees that the training dataset includes the same features and transformations that will be available at inference time, reducing training-serving skew.
- ✗
Disable online store publishing to avoid data duplication and reduce costs.
Why it's wrong here
Disabling online store publishing would prevent features from being available for real-time serving, breaking the ability to serve models that rely on online features. This practice would actually harm training-serving consistency because the online store would not have the latest feature values. It is not a recommended practice for teams that need low-latency inference.
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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.