Databricks-ML-Assoc Model Development Practice Question
A data scientist is using Databricks Feature Store to train a model. They define a feature table with a primary key and a timestamp key, and they want to ensure that when they create a training set, only the latest feature values as of each label event are used to avoid label leakage. Which Databricks Feature Store method should they call to create the training set with point-in-time correctness?
⚠ Common exam trap
The trap here is assuming that reading a feature table and joining manually is equivalent to point-in-time lookup, when only create_training_set enforces temporal 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
✓
fs.create_training_set(df, feature_lookups=..., label="label", exclude_columns=[...])
create_training_set is the Feature Store API that performs point-in-time lookups using the timestamp key of feature tables, ensuring that only feature values available before each label event are used. This prevents label leakage and is the correct method for building training sets in Databricks Feature Store. Other methods either write, read, or inspect tables without temporal join logic.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
fs.get_feature_table(name="features")
Why it's wrong here
get_feature_table retrieves metadata about a feature table, such as its schema and primary keys, but does not generate a training set. It is useful for inspection or programmatic access to table properties. It cannot perform the temporal join required for point-in-time correctness, so it does not solve the label leakage concern. The scientist needs a method that combines labels and features with time awareness.
- ✗
fs.read_table(name="features")
Why it's wrong here
read_table reads a feature table as a Spark DataFrame but does not handle point-in-time joins or label alignment. It returns the latest snapshot of features, not the values as of each label event. If used directly, the scientist would have to manually implement time-travel logic, which is error-prone. This method is for inspection or batch scoring, not training set creation.
- ✓
fs.create_training_set(df, feature_lookups=..., label="label", exclude_columns=[...])
Why this is correct
The create_training_set method of the FeatureStoreClient performs point-in-time lookups by default when feature tables have a timestamp key. It joins the label DataFrame with feature tables using the timestamp to fetch only feature values that were valid at or before each label event, preventing leakage. This is the intended API for building training sets with time-travel semantics in Databricks Feature Store.
- ✗
fs.write_table(df, name="features", mode="overwrite")
Why it's wrong here
write_table writes a DataFrame to a feature table; it does not create a training set. It is used for populating or updating feature tables, not for joining labels with features. Using it here would not perform point-in-time lookups and could result in using future feature values, causing label leakage. The engineer needs a method that constructs a training set with temporal joins.
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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.