Databricks-ML-Assoc Model Development Practice Question
A data scientist is using Databricks Feature Store to create a training dataset for a model. They define a feature table with a primary key and a timestamp column. They then create a training set using create_training_set with the feature table and a label DataFrame. They notice that the training set contains null values for some features, even though the feature table has no nulls. What is the most likely reason for the nulls in the training set?
⚠ Common exam trap
The trap here is assuming that nulls indicate a join failure or data type mismatch, when the real cause is that the label timestamps are earlier than any available feature data.
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 label DataFrame contains timestamps that are earlier than the earliest timestamp in the feature table, so no feature values exist for those times.
In point-in-time feature lookups, Feature Store retrieves the most recent feature values as of the label timestamp. If the label timestamp precedes all feature timestamps for a given primary key, no feature value exists, resulting in nulls. This occurs when the feature table's time range does not cover the label events' times, often because the feature data starts later than the labels.
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 timestamp column in the feature table is not sorted in ascending order, causing point-in-time lookups to fail and return nulls.
Why it's wrong here
The timestamp column does not need to be sorted for point-in-time lookups. Feature Store handles the sorting internally when performing time series lookups. Unsorted timestamps would not cause nulls; the lookup is based on finding the latest feature value before the label timestamp, regardless of order.
- ✗
The feature table was created without specifying a timestamp column, so Feature Store cannot perform point-in-time lookups and returns nulls.
Why it's wrong here
If no timestamp column is specified, Feature Store cannot perform time series lookups, but the scenario states that a timestamp column is defined. The nulls are not due to missing timestamp definition; they are due to the label timestamps being outside the range of feature data.
- ✗
The primary key columns in the feature table and label DataFrame do not match in data type, causing join failures and nulls.
Why it's wrong here
Data type mismatches in primary keys would typically cause an error or a failed join, not silently produce nulls. Databricks Feature Store expects the join keys to be of compatible types; if they are not, it would raise an exception rather than fill with nulls. The nulls are more likely due to missing feature values for the given times.
- ✓
The label DataFrame contains timestamps that are earlier than the earliest timestamp in the feature table, so no feature values exist for those times.
Why this is correct
Point-in-time lookups retrieve the feature values that were valid at the time of the label event. If the label timestamp is earlier than any feature timestamp in the feature table, there is no feature data available, resulting in nulls. This is a common issue when the feature table does not cover the full time range of the labels.
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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.