Databricks-Spark-Assoc Troubleshooting and Tuning DataFrame Apps Practice Question
Which action should be taken to optimize a Spark application that performs multiple operations on the same DataFrame and shows evidence of redundant re-computations in the Spark UI DAG visualization?
⚠ Common exam trap
Candidates often mistake cache() for an action. They forget that cache() is a lazy transformation and must be followed by an action like count() or write() to actually materialize the data in memory.
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
✓
Use the cache() or persist() method on the DataFrame.
Persisting (or caching) a DataFrame instructs Spark to store the computed results of the DataFrame in memory or on disk. This is vital when the same DataFrame is accessed multiple times across different stages, as it prevents Spark from re-executing the entire lineage graph from the source, significantly reducing processing time and resource consumption in complex, multi-step analytical pipelines.
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 shuffle partitions.
Why it's wrong here
Increasing shuffle partitions is useful for managing parallelism and data distribution after a shuffle, but it does not address the redundant computation of a DataFrame. If a DataFrame is used repeatedly, Spark will calculate it again for every action unless it is explicitly cached.
- ✓
Use the cache() or persist() method on the DataFrame.
Why this is correct
Caching stores the materialized result of a DataFrame. When the application accesses the data again, Spark retrieves it from the cache rather than re-computing the entire lineage. This is the correct technique for eliminating redundant work when multiple downstream operations depend on the same intermediate DataFrame.
- ✗
Implement a custom partitioner for all joins.
Why it's wrong here
Custom partitioners are useful for optimizing joins by minimizing shuffle, but they do not solve the problem of re-computing a DataFrame. Even with a custom partitioner, Spark will re-evaluate the source transformations every time a new action is triggered on the same DataFrame object.
- ✗
Convert the DataFrame to a RDD.
Why it's wrong here
Converting a DataFrame to an RDD does not automatically cache the data. It merely switches the API used for transformation. Without an explicit call to cache or persist, the RDD will still trigger re-computation of the underlying lineage every time an action is called.
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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
This Databricks-Spark-Assoc 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-Spark-Assoc exam.