Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A developer is using Spark on Databricks and wants to understand how the Driver and Executors communicate during a job. Which two statements accurately describe this interaction? (Choose two.)
⚠ Common exam trap
The trap here is assuming Executors communicate directly with each other or that the Driver and Executors share a JVM, which is only true in local mode.
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 Driver collects results from Executors after tasks complete.
In Spark's architecture, the Driver coordinates job execution by scheduling tasks and sending them to Executors. Executors run these tasks and, upon completion, send results back to the Driver for actions that require aggregation. This two-way communication is fundamental. Executors do not communicate directly with each other for shuffle data; they use a shuffle service. Heartbeats are sent to the Cluster Manager, not the Driver. The Driver and Executors run in separate JVMs in cluster mode.
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 Driver collects results from Executors after tasks complete.
Why this is correct
When an action is triggered, the Driver collects results from Executors. For example, in a collect() action, Executors send their partial results back to the Driver, which aggregates them. This is a key interaction: the Driver initiates tasks, Executors run them, and then send results back to the Driver. This statement accurately describes the return communication path.
- ✓
The Driver schedules tasks and sends them to Executors for execution.
Why this is correct
The Driver is responsible for analyzing the job, creating a DAG, and dividing it into stages and tasks. It then schedules these tasks and distributes them to Executors. This is a core part of Spark's architecture: the Driver acts as the coordinator, while Executors perform the actual work. This statement accurately describes the communication flow from Driver to Executors.
- ✗
Executors send heartbeat messages to the Driver to report their status.
Why it's wrong here
While Executors do send heartbeat messages, they typically send them to the Cluster Manager, not directly to the Driver. The Driver monitors Executors through the Cluster Manager or via the SparkContext's executor allocation manager. Heartbeats are primarily for liveness detection by the Cluster Manager. This statement is incorrect because it misidentifies the recipient of heartbeats.
- ✗
The Driver and Executors share the same JVM for efficient communication.
Why it's wrong here
In Spark's cluster architecture, the Driver and Executors run in separate JVMs, often on different machines. This separation allows for distributed processing and fault isolation. In local mode, they may run in the same JVM, but that is not the typical cluster deployment. This statement is incorrect for the general case described.
- ✗
Executors communicate with each other directly to share intermediate data during a shuffle.
Why it's wrong here
During a shuffle, Executors write shuffle files to local disk or a shuffle service, and other Executors fetch these files over the network. They do not communicate directly with each other; instead, they interact with the Driver for scheduling and with the shuffle service for data. Direct executor-to-executor communication is not a standard part of Spark's architecture.
About these practice questions
One of 295 original Databricks-Spark-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.