Databricks-DE-Pro Data Transformation, Cleansing, Quality Practice Question
You are performing a complex data transformation involving a self-join on a large, skewed table. Which technique is most effective for preventing data skew and improving join performance?
⚠ Common exam trap
Candidates often suggest increasing cluster size or memory, which fails to address the underlying data distribution problem causing skewed partitions in the join operation.
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
✓
Apply a 'salt' to the join key to redistribute the skewed data across more partitions.
Skewed joins are a primary cause of performance failure in distributed computing. Using a 'salted' key—adding a random prefix to the join key—distributes data evenly across partitions during the join. This prevents 'hot' partitions where one executor processes the majority of the data, significantly speeding up the query and preventing OOM (Out of Memory) errors, ensuring consistent performance for large-scale analytical tasks.
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 number of executors in the cluster to handle the load.
Why it's wrong here
Adding more executors does not solve the underlying skew problem. If data is skewed, one executor will still get the vast majority of the data regardless of how many others are available. The workload will remain bottlenecked by the single slowest task, regardless of total cluster size.
- ✗
Use the 'repartition' hint on the join keys to force uniform distribution.
Why it's wrong here
Repartitioning by the join key does not solve skew; it actually exacerbates it. If certain keys occur much more frequently than others, repartitioning will simply group all those heavy keys onto the same partition, leading to the exact same skew issues that you were trying to resolve initially.
- ✓
Apply a 'salt' to the join key to redistribute the skewed data across more partitions.
Why this is correct
Salting adds a random factor to the join key, which breaks up the large, heavy partitions caused by skewed values. By distributing the data across more executors, you ensure that no single task is overwhelmed, which is the most effective way to eliminate skew and improve join performance.
- ✗
Convert the join into a broadcast join to prevent shuffling entirely.
Why it's wrong here
Broadcast joins are only effective when one of the tables is small enough to fit in memory. If the skewed table is large, a broadcast join will cause an out-of-memory error. This technique is not applicable to the problem of a large, skewed table, making it an ineffective solution here.
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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
This Databricks-DE-Pro 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-DE-Pro exam.