Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
A data analyst is troubleshooting a slow Databricks SQL query that joins a large fact table with a small dimension table. The query plan shows a broadcast hash join, but the analyst notices that the small table is not being broadcast as expected. Which configuration should the analyst check to ensure the small table is broadcast?
⚠ Common exam trap
The trap here is assuming that enabling adaptive query execution alone guarantees a broadcast join, when the auto-broadcast threshold must also be satisfied.
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
✓
spark.sql.autoBroadcastJoinThreshold
The broadcast hash join is governed by the autoBroadcastJoinThreshold configuration, which defines the maximum size for a table to be broadcast. If the small table exceeds this threshold, it will not be broadcast. Checking and potentially increasing this threshold can enable the broadcast, improving performance. Other settings affect different aspects of query execution and do not directly control broadcast eligibility.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
spark.sql.adaptive.enabled
Why it's wrong here
This setting enables adaptive query execution, which can dynamically optimize joins at runtime, including converting sort-merge joins to broadcast joins if one side is small enough. However, it relies on the auto-broadcast threshold. If the threshold is too low, adaptive execution may still not broadcast. The analyst should first check the threshold, making this option less direct.
- ✓
spark.sql.autoBroadcastJoinThreshold
Why this is correct
This Spark configuration sets the maximum size in bytes for a table to be considered for broadcasting in a join. If the small table's size exceeds this threshold, it will not be broadcast. The analyst should verify this value and adjust it if necessary to allow the small table to be broadcast, which can improve join performance by avoiding a shuffle.
- ✗
spark.databricks.delta.optimizeWrite.enabled
Why it's wrong here
This configuration controls whether Delta Lake optimizes write operations by reducing the number of small files. It has no impact on join strategies or broadcast behavior. The analyst's issue is about query execution, not write optimization, so this setting is irrelevant.
- ✗
spark.sql.shuffle.partitions
Why it's wrong here
This configuration controls the number of partitions used when shuffling data for joins or aggregations. While it affects performance, it does not determine whether a table is broadcast. Even with an optimal partition count, a table will not be broadcast if it exceeds the auto-broadcast threshold. Thus, it is not the correct setting to check for broadcast behavior.
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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
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