Databricks-Spark-Assoc Spark Architecture and Components Practice Question
Which TWO of the following statements accurately describe the role of the Spark Executor in a cluster deployment?
⚠ Common exam trap
Candidates frequently attribute cluster coordination, DAG scheduling, and global metadata management to executors, forgetting that executors strictly execute tasks and cache data blocks.
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
✓
Executors execute the tasks dispatched by the Driver.
Executors are the workhorses of the Spark architecture, responsible for executing tasks and storing data. Recognizing their duality—compute and storage—is critical for tuning Spark applications. If executors are improperly sized, they can lead to OOM errors or underutilization of cluster resources. Understanding how executors manage task parallelism and block storage allows developers to optimize memory settings and partition counts effectively for high-performance data processing pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Executors are responsible for scheduling tasks across the worker nodes.
Why it's wrong here
Task scheduling is performed by the Driver, which determines the optimal distribution of work based on data locality and available resources. Executors do not perform scheduling; they merely receive task definitions from the Driver and execute the serialized code provided within those tasks on their assigned compute slots.
- ✓
Executors execute the tasks dispatched by the Driver.
Why this is correct
Executors serve as the execution environment for tasks assigned by the Driver. Upon receiving a task, the executor deserializes the code and executes it against the data partitions stored on or fetched to the worker node, reporting the task status and metrics back to the Driver periodically.
- ✓
Executors perform the storage of data in memory or on disk.
Why this is correct
Executors manage the BlockManager, which handles the caching of RDDs, DataFrames, and temporary shuffle files. This storage capability is essential for operations like persist() or cache(), allowing subsequent stages of the job to reuse computed data without re-processing, significantly reducing execution time for iterative algorithms or complex joins.
- ✗
Executors create the physical execution plan from user code.
Why it's wrong here
The creation of the physical execution plan is a core responsibility of the Catalyst Optimizer and the DAG Scheduler residing within the Driver. Executors have no knowledge of the overall query plan or the logical structure of the application; they only focus on completing individual units of work.
- ✗
Executors coordinate the cluster-wide resource allocation for the job.
Why it's wrong here
Resource allocation is managed by the Cluster Manager, which negotiates with the underlying resource manager to provide containers for executors. Executors are passive recipients of these resources and do not participate in the negotiation or allocation process, as they are managed entities within the allocated container space.
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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.