Databricks-DA-Assoc Data Modeling with Databricks SQL Practice Question
A data analyst is designing a star schema in Databricks SQL to optimize query performance for a large sales dataset. Which strategy most effectively minimizes data shuffling during join operations between a large fact table and a small dimension table?
⚠ Common exam trap
Candidates often suggest partitioning or Z-Ordering for every scenario. They miss that the broadcast join is specifically intended to eliminate shuffling by moving small tables instead of large ones.
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
✓
Leverage the BROADCAST join hint on the dimension table.
Utilizing the broadcast join strategy is essential when joining a massive fact table with a significantly smaller dimension table. By distributing the small table to all worker nodes, Databricks eliminates the need for expensive network shuffles of the fact table rows. This approach is fundamental for maintaining low latency in BI dashboards where users require sub-second query responses on complex star schema structures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply the CLUSTER BY clause on the primary key of the fact table.
Why it's wrong here
Clustering the fact table improves data skipping and storage organization but does not inherently optimize joins with smaller tables. While it enhances overall table scans, it fails to address the network overhead of shuffling large datasets across the cluster nodes during join operations, which broadcast joins solve more efficiently.
- ✗
Use the Z-ORDER BY clause on the dimension table's primary key.
Why it's wrong here
Z-Ordering is an excellent technique for optimizing data skipping on specific columns, particularly for range queries or equality filters. However, it does not influence the join strategy chosen by the Catalyst optimizer. Shuffling remains the default behavior for large-to-large joins regardless of Z-Order indices applied to the tables.
- ✓
Leverage the BROADCAST join hint on the dimension table.
Why this is correct
The broadcast hint forces the optimizer to send a copy of the smaller dimension table to every executor node. This prevents the large fact table from being repartitioned or shuffled across the network, significantly reducing join execution time. This is the optimal configuration for star schemas in Databricks SQL environments.
- ✗
Convert the fact table into a temporary view before joining.
Why it's wrong here
Converting a table to a temporary view is purely a syntactic abstraction and does not alter the physical execution plan or the underlying join algorithm. The optimizer treats the view essentially like a table, meaning standard shuffling mechanisms will still occur unless specific broadcast instructions or hints are explicitly provided.
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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.