Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A developer is using Spark on Databricks and wants to monitor the progress of a job. They need to understand how the driver coordinates with executors. Which component is responsible for scheduling tasks onto executors and tracking their status?
⚠ Common exam trap
Many exam-takers confuse the DAG Scheduler with the Task Scheduler, as both are involved in scheduling but at different levels of granularity.
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 Task Scheduler
The Task Scheduler in Spark is responsible for assigning tasks to executors and monitoring their execution. It works closely with the DAG Scheduler, which breaks the job into stages, but the Task Scheduler handles the actual task-level scheduling and status tracking. This makes it the component that directly coordinates with 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 Cluster Manager
Why it's wrong here
The Cluster Manager (e.g., YARN, Kubernetes, or Databricks' internal manager) allocates resources like executors to the Spark application. It does not schedule tasks within the application; that is the role of the Task Scheduler. The Cluster Manager operates at a higher level, managing the cluster's resources.
- ✗
The DAG Scheduler
Why it's wrong here
The DAG Scheduler creates a physical execution plan from the logical DAG and divides it into stages. It does not directly schedule tasks onto executors; that responsibility falls to the Task Scheduler. The DAG Scheduler handles stage-level scheduling and dependencies, not the fine-grained task assignment.
- ✗
The Catalyst Optimizer
Why it's wrong here
The Catalyst Optimizer is part of Spark SQL and is responsible for optimizing logical plans into physical plans. It does not handle task scheduling or executor coordination. Its role is to improve query performance through rule-based and cost-based optimizations, not runtime scheduling.
- ✓
The Task Scheduler
Why this is correct
The Task Scheduler is responsible for scheduling individual tasks onto executors based on data locality and resource availability. It tracks task status and retries failed tasks. This component directly manages the execution of tasks on the cluster, making it the correct answer for coordinating with executors.
About these practice questions
Courseiva writes every Databricks-Spark-Assoc question from scratch — 295 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. 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.