Databricks-Spark-Assoc Spark Architecture and Components Practice Question
What is the function of the 'Executor' within the Spark execution model?
⚠ Common exam trap
Many candidates believe the Driver executes the actual data processing tasks, confusing job planning responsibilities with worker-level computation and caching.
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
✓
It executes tasks assigned by the Driver and caches data.
The executor is the component that performs the actual computation and stores data. It is important to know that executors are distributed entities—they carry out the work assigned by the Driver. By understanding that executors run tasks in parallel and manage the cache, developers can configure their cluster appropriately for the memory and compute needs of their specific data processing pipelines, ensuring high throughput and efficient resource utilization throughout the application's lifecycle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It acts as the central coordinator for all worker nodes.
Why it's wrong here
The central coordinator is the Spark Driver, not the executor. The executor is a subservient process that follows the instructions provided by the Driver. It does not communicate with other executors directly to coordinate work, as all synchronization and job-flow management are handled centrally by the Driver's DAG scheduler.
- ✓
It executes tasks assigned by the Driver and caches data.
Why this is correct
The executor is a JVM process running on a worker node that executes the code dispatched by the Driver. It manages its own local memory for caching and processing, reports task status back to the Driver, and ensures that the assigned tasks are completed using the allocated CPU and memory resources.
- ✗
It initializes the SparkSession for the user's application.
Why it's wrong here
The SparkSession is initialized on the Driver node. The executor receives the serialized configuration and application context from the Driver, but it does not perform the initialization of the session itself. The session acts as the entry point for the user, and the executor is merely an implementation detail.
- ✗
It defines the logical plan of the Spark application.
Why it's wrong here
The logical plan is created by the Spark Driver using the Catalyst Optimizer. Executors have no knowledge of the overall logical plan; they are only responsible for the execution of discrete tasks and have no involvement in the high-level planning, optimization, or translation of the user's Spark code.
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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.