Databricks-Spark-Assoc Using Spark SQL Practice Question
When joining two large tables in Spark SQL, which join type helps avoid expensive shuffles by loading a small table into memory on all executor nodes?
⚠ Common exam trap
Candidates often select Sort-Merge Join or Shuffle Hash Join when trying to avoid network shuffles, forgetting that only broadcasting eliminates network movement for 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
✓
Broadcast Hash Join
A Broadcast Hash Join is a highly efficient join strategy in Spark SQL. By marking the smaller table as a broadcast candidate, Spark sends a complete copy of that table to every executor node. This allows the join to be performed locally at each node, eliminating the need to shuffle the large table across the network, which is often the primary bottleneck in large-scale distributed join operations.
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 Join involves redistributing both tables across the cluster based on the join key. This requires a full shuffle of data, which involves significant network I/O and serialization costs. It is generally used when both datasets are too large to fit in memory on a single node.
- ✓
Broadcast Hash Join
Why this is correct
Broadcast Hash Join optimizes performance by broadcasting the smaller table to all executors. Because the small table is available locally on every node, the join operation avoids large data shuffles, making it significantly faster for scenarios where one table can comfortably fit within the driver-defined broadcast memory threshold.
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Sort Merge Join
Why it's wrong here
Sort Merge Join is the default join type in Spark for large datasets. It requires both tables to be sorted and shuffled by the join key. While reliable for very large data, it involves high shuffle overhead and disk spilling, which is slower than broadcasting for small-to-large joins.
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Cartesian Product Join
Why it's wrong here
A Cartesian Product Join generates a combination of every row from both tables. It is extremely resource-intensive and computationally expensive. It is almost never the intended strategy for general joins and usually indicates an error in the join condition, potentially leading to 'out of memory' errors in production.
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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.