Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
A job is failing with a 'Disk Space' error on the worker nodes. The code performs several large joins. Which configuration should the engineer adjust to mitigate the disk space usage?
⚠ Common exam trap
Candidates often suggest 'increasing executor memory' as the first step, which is a costly infrastructure change compared to the performance tuning of shuffle partitions for memory-intensive joins.
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
✓
Increase spark.sql.shuffle.partitions.
Large joins often require spilling to disk when the shuffle data exceeds the available executor memory. Adjusting the spark.sql.shuffle.partitions configuration can reduce the size of individual partitions, potentially fitting them within memory. Alternatively, increasing the instance type memory or optimizing the join condition reduces the reliance on local disk storage. Managing shuffle partitions is a standard practice to balance memory pressure against the risk of disk overflow during shuffles.
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 spark.sql.shuffle.partitions.
Why this is correct
Increasing the number of shuffle partitions creates smaller, more manageable chunks of data. By reducing the size of individual partitions being joined, it becomes more likely that the operation can complete in memory, thereby avoiding the need to spill data to the local disk of worker nodes.
- ✗
Decrease spark.driver.memory.
Why it's wrong here
The driver node manages the job coordination and metadata. Reducing driver memory limits the capacity for handling the job's plan and metadata. It has no effect on the memory available to worker nodes for performing joins or managing shuffle data, and it may cause the job to fail.
- ✗
Set spark.databricks.io.cache.enabled to false.
Why it's wrong here
The Databricks IO cache improves read performance from cloud storage. Disabling it would not mitigate disk space issues on worker nodes during joins; rather, it would likely degrade overall job performance and increase the amount of data transferred from remote storage, potentially making the process slower.
- ✗
Increase the number of task retries.
Why it's wrong here
Increasing task retries only helps if a job fails due to transient network issues or temporary resource spikes. It does not resolve a systemic failure caused by insufficient disk space for shuffle operations. Repeating a failing operation will likely result in the same disk error every time.
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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-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-DE-Assoc exam.