Databricks-Spark-Assoc Spark Architecture and Components Practice Question
When a Spark application is running in Databricks, what determines the number of tasks that can run in parallel?
⚠ Common exam trap
Candidates often confuse 'number of executors' with 'parallelism.' While more executors help, the actual number of concurrent tasks is strictly limited by the total count of available CPU cores.
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
✓
The number of CPU cores available across the executors.
The number of tasks that run in parallel is determined by the number of slots available on the executors. Each executor provides a certain number of CPU cores, and each core can typically handle one task at a time. This relationship between CPU cores, executor memory, and task parallelism is vital for optimizing Spark jobs, as it directly influences how efficiently a cluster processes large-scale data sets and transformations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The total number of nodes in the cluster.
Why it's wrong here
While having more nodes increases capacity, it is the number of cores per executor that defines the task slots. You could have many nodes with very few cores, resulting in lower parallelism than a smaller cluster with high-core-count nodes, making the core count the primary determinant.
- ✓
The number of CPU cores available across the executors.
Why this is correct
Spark parallelism is directly tied to the number of available cores. Each core can process one task simultaneously. By increasing the number of cores per executor or the number of executors in the cluster, you directly increase the application's ability to process data in parallel, reducing runtime.
- ✗
The amount of driver memory available.
Why it's wrong here
Driver memory is used for task scheduling, metadata, and broadcast variables. It does not directly affect the number of parallel tasks that can run on executors. While an underpowered driver can slow down task submission, it is not a direct factor in defining the cluster's parallel task capacity.
- ✗
The size of the source data files.
Why it's wrong here
Data size influences the number of partitions created during initial ingestion, but it does not dictate how many tasks can run in parallel. Parallelism is limited by hardware resources (cores), not by the volume of data being processed, although partitioning strategies do impact how tasks are distributed.
About these practice questions
Courseiva writes every Databricks-Spark-Assoc question from scratch — 295 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.