Databricks-ML-Assoc Databricks Machine Learning Practice Question
A machine learning engineer is using Databricks Feature Store to create a training dataset for a model that predicts customer lifetime value. The feature table includes a timestamp key. Which TWO statements are true regarding point-in-time correctness when creating the training set? (Choose two.)
⚠ Common exam trap
The trap here is assuming that Feature Store automatically uses the latest feature values or that it includes future data, when it actually enforces strict temporal joins.
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 training set creation process uses the timestamp key to join features as of the label event time, preventing data leakage.
Point-in-time correctness in Feature Store requires a timestamp key and uses it to join features as of the label event time. This prevents data leakage by ensuring only past data is used. The other options incorrectly describe behavior that would either ignore timestamps or introduce future data, which are not how Feature Store operates.
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 training set will include feature values that were recorded after the label event time if they are available.
Why it's wrong here
Including future feature values would introduce leakage. Feature Store enforces point-in-time correctness by only using feature values with timestamps less than or equal to the label event time. Thus, this statement is incorrect because it violates the principle of avoiding future data.
- ✓
The training set creation process uses the timestamp key to join features as of the label event time, preventing data leakage.
Why this is correct
Feature Store uses the timestamp key to perform an as-of join, ensuring that for each label event, only feature values with timestamps up to that event are used. This prevents leakage and simulates real-time inference conditions. This is the core benefit of point-in-time correctness.
- ✗
If multiple feature values exist for an entity at the same timestamp, the training set will randomly select one.
Why it's wrong here
Feature Store resolves duplicates deterministically, typically by taking the latest value if timestamps are identical, or it may raise an error. Random selection would be non-reproducible and is not how the system behaves. Therefore, this statement is false.
- ✓
A timestamp key must be specified in the feature table to enable point-in-time lookups.
Why this is correct
To perform point-in-time correctness, the feature table must have a timestamp column designated as the timestamp key. This allows Feature Store to retrieve the correct historical feature values for each row in the training set based on the event time. Without it, time travel is not possible.
- ✗
The training set will automatically include the latest feature values for each entity, regardless of the timestamp.
Why it's wrong here
Feature Store does not default to latest values; it uses the timestamp key to perform time travel and retrieve feature values as of the specified timestamp. Ignoring timestamps would cause data leakage. Therefore, this statement is false because point-in-time correctness requires respecting the timestamp.
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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.