Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
A data engineer is optimizing a Databricks job that reads from a large Delta table and performs a join with a smaller table. The job is experiencing performance issues due to shuffling. The engineer wants to reduce the amount of data shuffled during the join. Which technique should the engineer use?
⚠ Common exam trap
The trap here is assuming that increasing shuffle partitions or repartitioning will reduce shuffle volume, when in fact they can increase it; the key is to avoid shuffling the large table 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
✓
Broadcast the smaller table using a broadcast hint.
Broadcasting the smaller table eliminates the need to shuffle the larger table. The smaller table is replicated to all worker nodes, and the join is performed locally, which drastically reduces network I/O and improves performance. This is the most effective technique when one side of the join is small enough to fit in memory.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Broadcast the smaller table using a broadcast hint.
Why this is correct
Broadcasting the smaller table avoids shuffling the larger table. The smaller table is sent to all worker nodes, and the join is performed locally on each node. This eliminates the shuffle of the large table, significantly reducing network overhead and improving performance. This is a standard optimization for joins where one table is small enough to fit in memory.
- ✗
Increase the number of shuffle partitions to 2000.
Why it's wrong here
Increasing shuffle partitions can help with skew and parallelism, but it does not reduce the total amount of data shuffled. In fact, more partitions can lead to more small files and increased overhead. The goal is to avoid shuffling the large table altogether, which broadcast join achieves.
- ✗
Enable Adaptive Query Execution (AQE) and set spark.sql.adaptive.enabled to true.
Why it's wrong here
AQE can optimize join strategies at runtime, including converting sort-merge joins to broadcast joins if statistics indicate one side is small. However, AQE may not always choose broadcast join, and relying on it alone is less deterministic than explicitly broadcasting the smaller table. For guaranteed avoidance of shuffle, an explicit broadcast hint is preferred.
- ✗
Repartition the larger table on the join key before the join.
Why it's wrong here
Repartitioning the larger table on the join key will still shuffle the entire large table, which is exactly what the engineer wants to avoid. While it can improve subsequent operations, it does not reduce the shuffle for this join. Broadcast join is more effective when one side is small.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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