Databricks-ML-Assoc Databricks Machine Learning Practice Question
A data scientist is using Databricks Feature Store to create a feature table for a recommendation model. They want to ensure that the feature table can be used for both training and batch scoring, and that it supports point-in-time correctness. Which two actions must they take when creating the feature table? (Choose two.)
⚠ Common exam trap
The trap here is assuming that time travel must be manually enabled or that partitioning is required, rather than focusing on the key definitions.
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
✓
Define a primary key for the feature table.
To create a feature table in Databricks Feature Store that supports training, batch scoring, and point-in-time correctness, the data scientist must define a primary key and a timestamp key. The primary key identifies entities for joins, and the timestamp key enables time-aware joins to prevent leakage. Other actions like enabling time travel manually, partitioning, or using streaming tables are not required and may be unnecessary or counterproductive. Thus, the correct choices are defining a primary key and specifying a timestamp key.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the table as a streaming table.
Why it's wrong here
Setting the table as a streaming table is not a requirement for Feature Store feature tables. Feature tables can be batch or streaming, but for training and batch scoring, a standard Delta table is sufficient. Streaming tables are used for real-time updates, but the scenario does not require streaming ingestion. The essential elements are primary key and timestamp key for point-in-time correctness.
- ✓
Define a primary key for the feature table.
Why this is correct
Defining a primary key is essential for a feature table in Databricks Feature Store. The primary key uniquely identifies each entity (e.g., user_id, item_id) and is used to join features to training data and for online serving. Without a primary key, the feature table cannot be properly registered or used for lookups, and point-in-time correctness relies on the key to match features to the correct entity at the correct time.
- ✗
Partition the feature table by the primary key.
Why it's wrong here
Partitioning by the primary key is not a requirement and can be inefficient if the primary key has high cardinality. Feature Store does not require partitioning for functionality. While partitioning can improve query performance in some cases, it is not necessary for training or batch scoring, and improper partitioning can lead to many small files. The focus should be on primary key and timestamp key definitions.
- ✓
Specify a timestamp key for point-in-time correctness.
Why this is correct
Specifying a timestamp key is necessary to achieve point-in-time correctness. The timestamp key indicates when each feature value was recorded. During training, Feature Store uses this to join the correct feature values as of the label timestamp, preventing data leakage. Without a timestamp key, point-in-time correctness cannot be guaranteed, and the feature table may not support time-aware joins.
- ✗
Enable time travel on the feature table.
Why it's wrong here
Enabling time travel on the underlying Delta table allows you to query historical versions, but it is not a required action when creating a feature table. Databricks Feature Store automatically manages time travel for point-in-time correctness if a timestamp key is provided. However, the user does not need to manually enable time travel; it is a built-in capability of Delta Lake that Feature Store leverages.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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