Databricks-Spark-Assoc Spark Architecture and Components Practice Question
What is the primary role of the 'Cluster Manager' in Spark?
⚠ Common exam trap
Candidates mistakenly believe the Cluster Manager controls the job's internal execution logic, rather than acting solely as a resource provider for the Spark application.
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
✓
It handles the physical allocation of resources like CPU and memory.
The Cluster Manager, such as Kubernetes or YARN, acts as the resource broker for the Spark application. It is vital to understand that it does not manage the Spark execution logic (the Driver does that). Instead, it provides the 'raw materials'—the executor containers—that the Driver needs to run tasks. Misunderstanding this can lead to incorrect assumptions about where failures occur: application logic failures happen in the Driver/Executors, while resource availability issues happen in the Manager.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It manages the Spark DAG and optimizes the query plan.
Why it's wrong here
The DAG and query optimization are managed entirely by the Spark Driver. The Cluster Manager is agnostic to the application's logical structure or the transformations being performed; its responsibility is solely focused on resource allocation and lifecycle management of the containerized processes on the worker nodes.
- ✓
It handles the physical allocation of resources like CPU and memory.
Why this is correct
The Cluster Manager interacts with the underlying infrastructure to negotiate resource requests made by the Driver. It allocates containers on nodes where the Spark executors can run, ensuring that the Spark application has the requested compute power and memory capacity to execute its tasks according to the configuration.
- ✗
It executes the tasks and stores intermediate shuffle data.
Why it's wrong here
Task execution and data storage are the responsibilities of the Spark Executors, which run inside the containers provided by the Cluster Manager. The Cluster Manager itself does not perform data processing or store intermediate results, as its role is limited to the infrastructure and provisioning layer of the architecture.
- ✗
It monitors the progress of individual Spark tasks.
Why it's wrong here
Task monitoring is a function of the Spark Driver. The Driver keeps track of which tasks have succeeded, failed, or are currently running. The Cluster Manager only monitors the health of the containerized processes it started, not the specific status or progress of the internal Spark tasks running within those containers.
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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.