Databricks-DA-Assoc Data Modeling with Databricks SQL Practice Question
When designing a star schema in Databricks SQL, why is it recommended to use Delta Lake for both Fact and Dimension tables?
⚠ Common exam trap
Candidates often choose standard Parquet tables assuming storage format has no impact on data warehouse updates, ignoring the necessity of transaction logs for dimensions.
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
✓
Delta Lake supports ACID transactions, which are necessary for reliable SCD updates.
Delta Lake provides ACID compliance and time travel, which are critical for maintaining the integrity of star schemas. Analytical workloads often rely on SCD (Slowly Changing Dimension) updates and complex joins. By using Delta Lake, you ensure that dimension updates are consistent and atomic, preventing users from seeing partial updates or corrupted data states, which is essential for accurate business intelligence reporting and historical analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It forces the use of Star Schema optimization, which is only supported for Delta format.
Why it's wrong here
Databricks SQL does not have a unique feature called 'Star Schema optimization' exclusive to Delta. While Delta Lake provides significant performance improvements and reliability, the star schema design pattern is a logical modeling choice that can be implemented across different formats, though Delta provides the best performance and consistency.
- ✓
Delta Lake supports ACID transactions, which are necessary for reliable SCD updates.
Why this is correct
SCD (Slowly Changing Dimension) patterns require atomic updates to history and status flags. Delta Lake ensures these operations are ACID compliant, meaning they are either fully committed or not committed at all. This prevents partial writes that could invalidate downstream analytical queries or lead to erroneous historical reporting.
- ✗
Delta Lake automatically reorders dimension tables to improve join performance.
Why it's wrong here
Delta Lake does not automatically reorder tables based on join patterns. While features like Z-Ordering can be applied manually to optimize performance, there is no inherent 'auto-reordering' feature for dimensions. Optimizing joins requires intentional data modeling choices such as clustering or selecting appropriate join algorithms for specific query types.
- ✗
It is required to store dimensions in the same schema as fact tables for performance.
Why it's wrong here
Physical location or logical schema placement does not impact join performance in Databricks. Join efficiency is determined by data distribution, shuffling, and the availability of statistics. Storing tables in the same schema is a organizational best practice, but it provides no inherent technical performance benefit for cross-table 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-DA-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-DA-Assoc exam.