Databricks-ML-Assoc ML Workflows Practice Question
A team trains a model with Databricks Feature Store features and logs the training set using feature_lookups. At inference time, they want the model to automatically retrieve the same feature values from the online store so the serving endpoint does not require the caller to supply those features. What must the team do when logging the model so this automatic lookup works?
⚠ Common exam trap
The trap here is believing that registry access grants or feature table publication enable automatic feature retrieval, when only recorded feature lookups in the logged model provide that behavior.
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 the feature engineering client's log_model and include the feature_lookups used during training
When a model is logged through the feature engineering client with the same feature lookups used during training, the model metadata carries the information needed to resolve those features from the online store at serving time. Callers then only supply primary keys, and the endpoint assembles the full feature vector automatically, keeping training and inference feature logic consistent.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Wrap the model in a PythonModel that calls the Feature Store client inside its predict method
Why it's wrong here
A custom PythonModel can fetch features manually, but that requires writing and maintaining lookup logic in predict and is not the mechanism that provides automatic lookup from recorded training lookups. The intended approach is to log the model with the feature lookups so the serving stack resolves features itself, rather than hand-coding retrieval in a custom wrapper.
- ✗
Publish the feature table with a primary key and enable streaming on the source Delta table
Why it's wrong here
Publishing a feature table and enabling streaming affects how feature data is stored and refreshed, not how a logged model requests features at inference time. Automatic feature lookup depends on lookup metadata stored with the model, so these storage-side actions alone will not let the endpoint retrieve features without the caller passing them.
- ✓
Log the model with the feature engineering client's log_model and include the feature_lookups used during training
Why this is correct
The feature engineering client's log_model records the feature lookups alongside the model artifact, so the model metadata knows which feature tables and lookup keys to use. When the model is served with online store access, the endpoint resolves those features itself, meaning callers only provide the primary keys and the model fetches the remaining feature values automatically.
- ✗
Register the model in Unity Catalog and grant the serving endpoint EXECUTE on the registered model
Why it's wrong here
Unity Catalog registration and grants control access to the model artifact and its versions, but they do not embed feature lookup instructions into the model. Without recorded feature lookups, the serving endpoint has no knowledge of which feature tables or keys to query, so it still requires the caller to supply all feature values explicitly.
Visual reference
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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
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