Databricks-Spark-Assoc Using Spark SQL Practice Question
Which of the following describes the behavior of a 'Broadcast Hash Join' in Spark SQL?
⚠ Common exam trap
Students often assume both tables are broadcast or that a broadcast join requires shuffling both datasets, overlooking the core design of sending only the small table.
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 smaller table is sent to all executors, avoiding a shuffle of the large table.
A Broadcast Hash Join is a highly efficient join strategy where the smaller table is sent to all worker nodes, allowing the join to occur locally in memory. This eliminates data shuffles, which are usually the slowest part of a distributed join. Understanding when the optimizer chooses this—and when to force it—is essential for optimizing SQL performance in Databricks environments where network bandwidth is a common bottleneck.
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 larger table is shuffled to match the smaller table's partitions.
Why it's wrong here
This describes a Shuffle Hash Join or Sort Merge Join. In a Broadcast Hash Join, the smaller table is the one that is moved, not the larger one. Moving the large table would defeat the purpose of the optimization and likely lead to excessive network congestion and memory exhaustion on the executors.
- ✓
The smaller table is sent to all executors, avoiding a shuffle of the large table.
Why this is correct
By broadcasting the smaller table, Spark eliminates the need to move the large dataset across the network. The large table's partitions are processed in parallel on each executor against a full local copy of the small table. This is the most efficient join type for small-to-large table operations.
- ✗
Both tables are shuffled to a common partition based on the join key.
Why it's wrong here
This is a Shuffle Sort Merge Join. While robust for joining two large tables, it is significantly slower than a Broadcast Hash Join for cases where one table is small. Shuffling both tables causes massive network I/O, which is exactly what we aim to avoid using the broadcast join strategy.
- ✗
The join is performed entirely on the driver node.
Why it's wrong here
No join operation in Spark is performed entirely on the driver node. The driver's role is to coordinate the execution plan and collect final results. Attempting to perform joins on the driver would be impossible for any non-trivial dataset and would immediately crash the application due to driver memory limitations.
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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-Spark-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-Spark-Assoc exam.