Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A Spark application is running on a cluster with 5 executors. The driver program creates a broadcast variable that is used in a transformation. Which two components are directly involved in distributing and using the broadcast variable? (Choose two.)
⚠ Common exam trap
The trap here is assuming that the Cluster Manager or Task Scheduler plays a role in broadcast variable distribution, when in fact it is handled entirely by the driver and 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
✓
The driver serializes the broadcast variable and sends it to each executor via a BitTorrent-like protocol.
Broadcast variables are distributed by the driver, which serializes and sends them to executors using an efficient broadcast protocol. Each executor then caches the variable and makes it available to all its tasks. This two-step process minimizes network traffic and ensures that the variable is easily accessible during task execution.
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 DAG Scheduler ensures that broadcast variables are only used in narrow transformations.
Why it's wrong here
The DAG Scheduler creates stages from the logical DAG but does not enforce constraints on where broadcast variables can be used. Broadcast variables can be used in both narrow and wide transformations. The DAG Scheduler's responsibility is stage partitioning, not variable usage restrictions.
- ✗
The Cluster Manager replicates the broadcast variable across all nodes in the cluster.
Why it's wrong here
The Cluster Manager is not involved in distributing broadcast variables. Its role is to allocate resources like executors to the Spark application. Broadcast variables are handled entirely by the Spark driver and executors, independent of the Cluster Manager's resource allocation duties.
- ✓
The driver serializes the broadcast variable and sends it to each executor via a BitTorrent-like protocol.
Why this is correct
The driver is responsible for serializing the broadcast variable and initiating its distribution. Spark uses an efficient broadcast mechanism, often BitTorrent-like, to disseminate the variable to executors without overwhelming the driver. This ensures that each executor receives the variable once and can share it with other executors if needed.
- ✗
The Task Scheduler assigns the broadcast variable to each task individually.
Why it's wrong here
The Task Scheduler schedules tasks onto executors but does not handle the distribution of broadcast variables. Broadcast variables are distributed once per executor, not per task. The Task Scheduler's role is to manage task execution, not data distribution.
- ✓
Each executor caches the broadcast variable in memory and makes it available to all tasks within that executor.
Why this is correct
Once an executor receives the broadcast variable, it stores it in memory (or disk if memory is insufficient) and shares it across all tasks running in that executor's JVM. This avoids re-sending the variable for each task, significantly reducing network overhead and improving performance.
About these practice questions
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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.