Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A data engineer is configuring a Spark application on Databricks. They set `spark.executor.instances` to 4, `spark.executor.cores` to 5, and `spark.executor.memory` to 16g. The cluster has 5 worker nodes, each with 16 cores and 64 GB RAM. What is the maximum number of tasks that can run concurrently across all executors?
⚠ Common exam trap
The trap here is multiplying the number of worker nodes by cores per executor instead of using the configured number of executors, or simply selecting the number of executors.
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
✓
20
The number of concurrent tasks in a Spark application is determined by the total number of cores allocated to executors. With 4 executors and 5 cores each, the total is 20 cores, allowing up to 20 tasks to run simultaneously. This is a fundamental relationship in Spark's architecture: each core can process one task at a time, so the total task concurrency equals the sum of cores across all executors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
20
Why this is correct
The maximum number of concurrent tasks equals the total number of executor cores in the cluster. With 4 executors and 5 cores per executor, the total is 4 × 5 = 20. Each core can run one task at a time, so up to 20 tasks can execute in parallel. This assumes no other resource constraints and that the cluster has enough capacity to launch all executors.
- ✗
80
Why it's wrong here
This would be the result of multiplying the number of worker nodes (5) by the cores per executor (5) and then by something else, but it incorrectly assumes all worker nodes are fully utilized or that each executor uses all cores. The configured number of executors is 4, not 5, and each executor uses 5 cores. The correct calculation is 4 × 5 = 20.
- ✗
5
Why it's wrong here
This is the number of cores per executor, not the total concurrent tasks. The total concurrency is calculated by multiplying the number of executors by the number of cores per executor. Here, 4 executors × 5 cores per executor = 20 concurrent tasks. The number 5 alone ignores the number of executors and would be correct only if there were a single executor.
- ✗
4
Why it's wrong here
This is the number of executors, not the number of concurrent tasks. Each executor can run multiple tasks in parallel based on its allocated cores. With 5 cores per executor and 4 executors, the total concurrency is 20 tasks. The value 4 would be correct only if each executor had exactly one core, which is not the case 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-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.