Databricks-Spark-Assoc Troubleshooting and Tuning DataFrame Apps Practice Question
Which configuration parameter should be adjusted to change the default number of partitions when reading from a shuffle-heavy operation?
⚠ Common exam trap
Candidates often confuse spark.sql.shuffle.partitions with spark.default.parallelism or source-specific reading options when trying to control shuffle stages.
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
✓
spark.sql.shuffle.partitions
The `spark.sql.shuffle.partitions` configuration is the primary setting used to control the parallelism of shuffle-based operations in Spark SQL. Adjusting this value allows developers to match the level of parallelism to the specific data volume and cluster resources. Understanding how this setting impacts performance is essential for fine-tuning Spark applications to prevent task contention and ensure that the cluster is fully utilized during data-intensive stages of a job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
spark.executor.memory
Why it's wrong here
This setting controls the heap memory available to the JVM on each executor. While it is vital for preventing OOM errors, it does not define the number of partitions created during a shuffle. Changing this will not affect the parallel distribution of data during join or aggregation stages.
- ✗
spark.default.parallelism
Why it's wrong here
This setting is primarily used for RDD-based operations where no partitioner is defined. In Spark SQL and DataFrame API contexts, spark.sql.shuffle.partitions is the authoritative setting that overrides default behavior, making this parameter irrelevant for optimizing the performance of modern DataFrame-based shuffle operations in production environments.
- ✓
spark.sql.shuffle.partitions
Why this is correct
This parameter explicitly defines the number of partitions to use when shuffling data for joins or aggregations. By tuning this value, developers can control the level of parallelism, which is crucial for balancing the trade-off between task overhead and executor utilization during resource-intensive stages of a Spark job.
- ✗
spark.driver.memory
Why it's wrong here
Driver memory is dedicated to the application's master node tasks, such as scheduling and task monitoring. It has no impact on the number of partitions created for shuffle tasks, which occur on worker nodes. Adjusting this will not influence the degree of parallelism for the shuffle stage.
About these practice questions
This Databricks-Spark-Assoc question is part of Courseiva's 295-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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-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.