Databricks-Spark-Assoc Spark Architecture and Components Practice Question
Which Spark component is responsible for maintaining the Directed Acyclic Graph (DAG) of stages and tasks?
⚠ Common exam trap
Candidates often guess the Cluster Manager or the DAGScheduler component separately, failing to realize the Driver is the overarching process that hosts these internal scheduling and planning components.
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
The Spark Driver is the central coordinator. It generates the DAG of stages based on the user's transformations and the physical execution plan. This DAG is essential for Spark to optimize query plans through catalyst and execute tasks in the correct dependency order. Understanding this architectural role is fundamental to knowing how Spark converts high-level DataFrame API calls into low-level distributed operations executed by the cluster.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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The Executor
Why it's wrong here
Executors are passive components that process tasks. They do not have visibility into the entire DAG or the ability to reorder stages. Their role is limited to executing the specific tasks sent to them by the driver and reporting status updates back to the scheduler.
- ✓
The Driver
Why this is correct
The Driver is responsible for translating user code into a DAG of stages. It optimizes this graph and decides the execution order of stages. By managing the DAG, the driver ensures that transformations are performed efficiently, minimizing shuffles and maximizing parallel processing performance across the distributed cluster environment.
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The Cluster Manager
Why it's wrong here
The cluster manager handles resource provisioning, not query planning. It does not understand DAGs or Spark-specific execution logic. Its role is strictly limited to allocating memory, CPUs, and nodes for the executors to run on, completely independent of how the Spark application structures its internal tasks.
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The Storage Manager
Why it's wrong here
The storage manager is a sub-component responsible for managing blocks of data in cache. It is not involved in DAG orchestration or query planning. It operates at a lower level, dealing with data persistence rather than the logical control flow of the distributed application's execution logic.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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