Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
An analyst needs to perform a join between a very large table and a small lookup table. Which optimization strategy should the analyst ensure is utilized to maximize performance?
⚠ Common exam trap
Candidates might suggest creating expensive indexes or partitioning the massive table, overlooking that broadcasting the small lookup table eliminates the shuffle bottleneck entirely.
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
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Broadcast Hash Join
Broadcast joins are the most efficient way to join a small table with a large table. By broadcasting the small table to all worker nodes, Databricks avoids expensive data shuffling, which is the most common bottleneck in distributed joins. Utilizing the query optimizer's ability to perform broadcast joins is a key skill for Databricks SQL analysts, as it drastically reduces network traffic and query latency in large-scale data processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Shuffle Hash Join
Why it's wrong here
Shuffle hash joins require moving data across the network to group data by the join key on specific workers. This is significantly slower than a broadcast join for small tables because it incurs heavy network I/O overhead that is unnecessary when one table can easily fit in memory.
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Sort Merge Join
Why it's wrong here
Sort merge joins involve sorting both tables before joining, which is computationally expensive and slow for this scenario. It is generally preferred for joining two large tables, but it is less performant than a broadcast join when one side of the join is small enough to fit in memory.
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Broadcast Hash Join
Why this is correct
Broadcast hash joins significantly improve performance by sending the small table to every node. This eliminates the need for shuffling the large table, which is the primary cause of latency in distributed joins. This strategy is the standard best practice for joining large datasets with small lookup tables.
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Cartesian Product Join
Why it's wrong here
A Cartesian product join results in an exponential number of rows and is computationally devastating for query performance. It is almost never the intended join strategy for lookup operations and will likely result in a query timeout or memory overflow error when dealing with large datasets in Databricks.
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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.