Databricks-DE-Pro Data Modelling Practice Question
A retail company wants to analyze sales by product, store, and date. The data team is designing the Gold layer and needs to choose between a star schema and a snowflake schema. Which factor most strongly favors a star schema in a Databricks Lakehouse?
⚠ Common exam trap
The trap here is assuming that star schemas reduce redundancy, but they actually increase redundancy to improve query performance.
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
✓
Star schemas simplify queries and improve performance by minimizing joins.
Star schemas minimize joins by denormalizing dimensions, which simplifies queries and boosts performance. In Databricks, this aligns well with Delta Lake and Photon optimizations. Snowflake schemas normalize dimensions, increasing joins and complexity. Storage and referential integrity are not primary advantages of star schemas.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Star schemas simplify queries and improve performance by minimizing joins.
Why this is correct
Star schemas use denormalized dimensions, which reduces the number of joins needed for analytical queries. This simplicity leads to faster query performance, especially in a Lakehouse where join operations can be expensive. Databricks' Photon engine and Delta Lake optimizations work well with star schemas. This is a key reason to choose a star schema.
- ✗
Star schemas reduce data redundancy by normalizing dimension tables.
Why it's wrong here
Star schemas actually denormalize dimension tables to reduce the number of joins, which increases redundancy but improves query performance. Normalization is characteristic of snowflake schemas. Therefore, this statement is incorrect and does not favor a star schema.
- ✗
Star schemas enforce referential integrity through foreign key constraints.
Why it's wrong here
While star schemas can have foreign key relationships, Delta Lake does not enforce referential integrity constraints by default. Snowflake schemas also do not inherently enforce them. This is not a distinguishing factor. The primary advantage of star schemas is query simplicity and performance.
- ✗
Star schemas require less storage space than snowflake schemas.
Why it's wrong here
Star schemas typically use more storage due to denormalization, as dimension attributes are repeated across rows. Snowflake schemas reduce storage by normalizing dimensions. Therefore, storage efficiency is not a reason to choose a star schema; in fact, it's the opposite.
About these practice questions
This Databricks-DE-Pro question is part of Courseiva's 267-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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-DE-Pro 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-DE-Pro exam.