Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
A data engineer is optimizing a Databricks job that processes a large dataset. The job performs a join between a large Delta table and a small dimension table, then writes the result to a Delta table. The engineer notices that the join is causing a large shuffle and wants to reduce shuffle overhead. Which two actions should the engineer take to improve performance? (Choose two.)
⚠ Common exam trap
The trap here is thinking that repartitioning or increasing shuffle partitions will reduce shuffle overhead, when the real solution is to avoid shuffling the large table by broadcasting the small one.
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
✓
Enable Adaptive Query Execution (AQE) to automatically convert the join to a broadcast join if applicable.
Broadcasting the small dimension table eliminates the need to shuffle the large table, as the small table is replicated to each executor for local joins. Enabling Adaptive Query Execution allows Spark to automatically convert sort-merge joins to broadcast joins when one side is small, further reducing shuffle. Together, these actions minimize shuffle overhead and improve join performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable Adaptive Query Execution (AQE) to automatically convert the join to a broadcast join if applicable.
Why this is correct
AQE can dynamically switch a sort-merge join to a broadcast join if it detects that one side of the join is small enough after initial stages. This reduces shuffle overhead by avoiding the shuffle of the large table. Enabling AQE allows Spark to optimize the join at runtime, complementing manual broadcast hints.
- ✓
Broadcast the small dimension table to avoid shuffling the large table.
Why this is correct
Broadcasting the small dimension table replicates it to all executors, allowing the join to be performed locally on each executor without shuffling the large table. This eliminates the shuffle of the large dataset, significantly reducing network overhead and improving join performance. Spark's broadcast join is ideal when one side is small enough to fit in memory.
- ✗
Repartition the large Delta table on the join key before the join.
Why it's wrong here
Repartitioning the large table on the join key can colocate matching rows, but it still requires a full shuffle of the large table. This adds overhead rather than reducing it. While it can improve subsequent operations, it does not eliminate the shuffle caused by the join. Broadcasting the small table avoids shuffling the large table altogether.
- ✗
Increase the number of shuffle partitions to distribute the join workload more evenly.
Why it's wrong here
Increasing shuffle partitions can help with parallelism, but it does not reduce the shuffle overhead itself. In fact, more partitions can lead to more small files and increased overhead. The goal is to avoid the shuffle entirely, not to redistribute it. Broadcasting the small table is a more effective solution for reducing shuffle.
- ✗
Cache the large Delta table in memory before the join to speed up reading.
Why it's wrong here
Caching the large table may improve read performance for subsequent operations, but it does not address the shuffle caused by the join. The join still requires shuffling the large table unless a broadcast join is used. Caching consumes memory and may not be feasible for very large tables. The focus should be on eliminating the shuffle.
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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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