Databricks-ML-Pro ML Ops Practice Question
A team is using Databricks Feature Store to manage features for a real-time fraud detection model. They need to ensure that the features used during training are consistent with those served at inference time. Which two actions should they take to achieve this? (Choose two.)
⚠ Common exam trap
The trap here is thinking that manual synchronization or custom UDFs can replace the Feature Store's automated consistency mechanisms, when they actually introduce 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
✓
Publish the model with the Feature Store, so that the model automatically looks up the latest feature values from the online store at inference time.
Using FeatureStoreClient.create_training_set ensures point-in-time correctness during training, and publishing the model with Feature Store ensures that inference uses the same feature lookups from the online store. Together, these actions guarantee that features are consistent between training and serving, which is critical for real-time fraud detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Publish the model with the Feature Store, so that the model automatically looks up the latest feature values from the online store at inference time.
Why this is correct
When you log a model with Feature Store, it records the feature lookups. At inference time, the model uses the online store to fetch the latest feature values, ensuring that the same feature transformations are applied. This maintains consistency between training and serving and is the recommended practice for real-time models.
- ✗
Manually copy the feature values from the offline store to the online store before each inference request to ensure freshness.
Why it's wrong here
Manual copying is error-prone and not scalable. Databricks Feature Store automatically syncs features from the offline store to the online store when you publish feature tables. Manual intervention would introduce latency and potential inconsistencies, defeating the purpose of the Feature Store.
- ✓
Use the FeatureStoreClient to create a training set that joins features from the feature tables, ensuring point-in-time correctness.
Why this is correct
The FeatureStoreClient.create_training_set method performs a point-in-time join, which ensures that only feature values available at the time of each label are used. This prevents data leakage and guarantees consistency between training and inference feature values, which is essential for real-time fraud detection.
- ✗
Implement a custom UDF in the model to compute features on the fly from raw data, bypassing the Feature Store entirely.
Why it's wrong here
Bypassing the Feature Store means you lose the consistency guarantees it provides. Custom UDFs might not replicate the exact transformations used during training, leading to training-serving skew. While on-the-fly computation can be useful, it does not ensure consistency and is not the purpose of the Feature Store.
- ✗
Train the model using a separate notebook that reads directly from the online store to simulate inference-time feature retrieval.
Why it's wrong here
The online store is designed for low-latency serving and may not contain historical data needed for training. Reading from the online store for training would not provide point-in-time correctness and could lead to data leakage or missing features. Training should use the offline store with point-in-time joins.
About these practice questions
Courseiva writes every Databricks-ML-Pro question from scratch — 300 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. 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.