Databricks-ML-Pro Model Development Practice Question
A data scientist is using Databricks Feature Store to build a training set for a fraud detection model. They define a feature table with a primary key of `transaction_id` and a timestamp key of `event_ts`. When creating the training set with `create_training_set`, they specify `lookup_key=['transaction_id']`. The resulting training set contains features from multiple feature tables. Which statement describes how point-in-time correctness is ensured during this operation?
⚠ Common exam trap
The trap here is assuming that a primary key join automatically prevents data leakage, when in fact the timestamp key is required to enforce point-in-time correctness.
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
✓
The timestamp key is automatically used to join features as of the timestamp of each label event, preventing data leakage from future feature values.
Point-in-time correctness in Databricks Feature Store is achieved by time-series joins using the timestamp key. When creating a training set, the system matches each label row with feature values whose timestamps are less than or equal to the label timestamp. This ensures that only historical, non-leaking features are used. The primary key alone is insufficient; the timestamp key is essential for temporal alignment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The `create_training_set` function automatically sorts features by their creation date and selects the latest values before the label timestamp.
Why it's wrong here
Feature Store does not sort by creation date; it uses the event timestamp from the feature table. Creation date is metadata about when the feature was written, not when the event occurred. Using creation date could include features computed after the label event, leading to leakage. The timestamp key must reflect the actual event time.
- ✓
The timestamp key is automatically used to join features as of the timestamp of each label event, preventing data leakage from future feature values.
Why this is correct
Databricks Feature Store uses the timestamp key to perform time-series joins, ensuring that for each label event, only feature values with timestamps at or before the label timestamp are included. This prevents leakage of future information and maintains point-in-time correctness, which is critical for fraud detection where future transactions could otherwise contaminate the training data.
- ✗
The primary key alone guarantees point-in-time correctness because it uniquely identifies each transaction and its associated features.
Why it's wrong here
The primary key ensures uniqueness and enables joins, but it does not prevent temporal leakage. Without using the timestamp key, features from later transactions could be incorrectly associated with earlier labels. Point-in-time correctness requires comparing timestamps to select the correct historical feature values, not just matching on a unique identifier.
- ✗
Point-in-time correctness is enforced only if the feature tables are registered with a `timestamp` column that matches the label DataFrame's index.
Why it's wrong here
Databricks Feature Store does not require the label DataFrame's index to match the timestamp column. Instead, it uses the timestamp key specified when creating the feature table and the timestamp column in the label DataFrame (if provided) to perform as-of joins. The index is not used for temporal alignment, and relying on it would be incorrect.
About these practice questions
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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.