Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A Spark application on Databricks uses a broadcast variable to distribute a large lookup table to all executors. The developer notices that the broadcast variable is not being used efficiently, as executors are still fetching the data multiple times. Which component is responsible for ensuring that the broadcast data is distributed only once per executor and cached there?
⚠ Common exam trap
The trap here is attributing broadcast distribution to the driver's Block Manager or the DAG Scheduler, when the executors' Block Managers are responsible for fetching and caching the broadcast data locally.
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 Executors' Block Managers
Broadcast variables are distributed using a BitTorrent-like protocol where the driver divides the data into blocks. Executors use their Block Managers to fetch these blocks from the driver or from other executors, and then cache the assembled broadcast data locally. This ensures that each executor retrieves the broadcast data only once, even if multiple tasks on that executor need it. The Block Manager is the key component for caching and serving broadcast blocks on 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.
- ✗
The Driver's Block Manager
Why it's wrong here
The Driver's Block Manager is part of the driver's memory management but is not responsible for distributing broadcast variables to executors. The driver initiates the broadcast by creating a `TorrentBroadcast` and dividing the data into blocks, but the actual distribution and caching on executors are handled by the executors' Block Managers. The driver's Block Manager may serve blocks, but it does not ensure once-per-executor caching.
- ✗
The DAG Scheduler
Why it's wrong here
The DAG Scheduler is responsible for stage creation and task scheduling based on lineage. It does not manage data distribution or caching of broadcast variables. Broadcast variables are handled by the broadcast manager and Block Managers. The DAG Scheduler only ensures that tasks are executed in the correct order; it does not deal with the mechanics of broadcasting data.
- ✓
The Executors' Block Managers
Why this is correct
Each executor has a Block Manager that manages cached data, including broadcast variables. When a broadcast variable is created, the driver divides it into blocks and informs executors. Executors fetch blocks from the driver or from other executors that already have them, and the Block Manager caches the assembled broadcast data on that executor. This ensures each executor fetches the data once and reuses it, reducing network overhead.
- ✗
The Cluster Manager
Why it's wrong here
The Cluster Manager allocates resources and launches executors, but it does not participate in data distribution for broadcast variables. Broadcast distribution is an internal Spark mechanism involving the driver and executor Block Managers. The Cluster Manager is unaware of Spark's internal data structures like broadcast variables and only manages the lifecycle of containers or pods.
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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.