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 key. After creating the training set, they notice that some feature values are missing in the output. What is the most likely cause?
⚠ Common exam trap
The trap here is assuming that missing feature values are always due to data quality issues, rather than the time range specified for the training dataset.
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 dataset was created using a time range that does not cover the timestamps of some feature values.
Feature Store uses point-in-time lookups based on the timestamp key to ensure training data reflects the state at the time of the label. If the training dataset's time range does not include the timestamps of certain feature values, those values will be missing. This is a common pitfall when defining the training set's time range too narrowly.
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 primary key of the feature table does not match the primary key of the training dataset.
Why it's wrong here
If the primary keys do not match, the join would fail entirely or produce no features, not just some missing values. Feature Store requires that the primary keys are consistent. While a mismatch would cause problems, it would typically result in an error or a completely empty feature set, not partial missing values. The scenario implies some features are present, so this is unlikely.
- ✗
The feature table was created without specifying a timestamp key, so point-in-time lookups could not be performed.
Why it's wrong here
The scenario states that a timestamp key was defined, so this is not the cause. If a timestamp key were missing, Feature Store would not be able to perform point-in-time lookups, but that is not the case here. Missing feature values are more likely due to the absence of matching records at the specified time, which can happen even with a timestamp key.
- ✗
The feature table was not refreshed after new data was added, so the latest feature values are not available.
Why it's wrong here
If the feature table is not refreshed, it may lack the most recent data, but the missing values could be due to the training dataset's time range rather than staleness. However, the question specifies that some feature values are missing, which is more directly explained by the time range not covering the timestamps. Staleness would affect all recent data, not just some values. Thus, this is less likely than the time range issue.
- ✓
The training dataset was created using a time range that does not cover the timestamps of some feature values.
Why this is correct
Feature Store performs point-in-time lookups based on the timestamp key. If the training dataset's time range does not encompass the timestamps of certain feature values, those values will be absent. This is a common issue when the feature table has data outside the specified range. The other options do not directly explain missing values in this context.
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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.