Databricks-Spark-Assoc Troubleshooting and Tuning DataFrame Apps Practice Question
A Spark job is experiencing data skew during a join operation on a key column. Which strategy is most effective for mitigating this issue without changing the business logic?
⚠ Common exam trap
Candidates often forget that when salting a join key on one table, the matching table must also be expanded or replicated to join all the salted variants.
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
✓
Add a random prefix to the join key of the skewed table and replicate the join key of the other table.
Salting the join key by appending a random integer helps distribute the skewed keys across multiple partitions. This prevents a single executor from handling the bulk of the data, which is the primary cause of long-running tasks in skewed joins. Understanding how to repartition data based on a salted key is critical for developers tasked with optimizing performance in distributed systems where key distribution is inherently uneven across the cluster nodes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the spark.sql.shuffle.partitions configuration value significantly.
Why it's wrong here
Increasing partitions helps parallelism but does not address the fundamental issue where a single key lands on one executor. If one key is massive, more partitions will not split that specific task, leaving the skew bottleneck in place while potentially adding unnecessary overhead to the cluster's task scheduling mechanism.
- ✗
Broadcast the larger table to all executor nodes.
Why it's wrong here
Broadcasting is intended for tables that fit into executor memory. Applying this to a large table will trigger an OutOfMemoryError as Spark attempts to load the entire dataset into the driver and executor heap memory, ultimately crashing the application rather than resolving the skew-related processing delay.
- ✓
Add a random prefix to the join key of the skewed table and replicate the join key of the other table.
Why this is correct
Salting distributes the skewed keys across different partitions by creating a composite key. By replicating the join key in the smaller table, you ensure that the original join condition is still satisfied while allowing Spark to process the previously bottlenecked key across several parallel tasks simultaneously.
- ✗
Cache the skewed table in memory before performing the join.
Why it's wrong here
Caching keeps data in memory but does not reorganize the distribution of data across partitions. The skewed key will still reside on one partition, meaning a single task will continue to process the majority of the data, failing to resolve the execution time imbalance caused by the data skew.
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JA
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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