Databricks-Spark-Assoc Spark Architecture and Components Practice Question
What is the primary function of the Spark DAG Scheduler?
⚠ Common exam trap
Candidates often confuse the DAG Scheduler with the Task Scheduler. They mistakenly believe the DAG scheduler manages low-level task execution on workers, rather than focusing on the logical stage boundary creation.
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
✓
Converting logical transformation chains into stages of tasks.
The DAG Scheduler is responsible for translating the logical plan into a physical execution plan, specifically breaking the lineage into stages based on shuffle boundaries. By identifying wide dependencies that require data redistribution, it organizes the execution flow. This is fundamental for optimizing performance, as it minimizes data movement across the network by grouping together all narrow dependency transformations into a single executable stage before triggering a shuffle operation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Managing low-level memory allocation for individual executors.
Why it's wrong here
Memory management is handled by the MemoryManager within each executor, not the DAG Scheduler. The Scheduler operates at a higher level, focusing on graph traversal and stage dependency resolution rather than the physical byte-level management of heap space or the caching policies within individual Spark worker containers.
- ✓
Converting logical transformation chains into stages of tasks.
Why this is correct
The DAG Scheduler analyzes the lineage of RDDs and DataFrames, identifying where shuffles occur. It groups operations that can be computed in parallel without data movement into stages. This allows Spark to build an efficient execution pipeline, reducing the need for disk I/O and increasing overall job speed.
- ✗
Resource negotiation with the Cluster Manager.
Why it's wrong here
Resource negotiation is handled by the Spark Driver's interface with the Cluster Manager (like YARN or K8s). The DAG Scheduler does not communicate with the external cluster hardware manager; its scope is strictly limited to the logical organization of the computation graph into runnable units for the task scheduler.
- ✗
Serializing data for network transmission between workers.
Why it's wrong here
Data serialization is performed by Spark's serialization libraries (like Kryo or Java serialization) at the executor level. The DAG Scheduler provides the plan for *when* data needs to be moved, but it does not perform the actual serialization or network communication that occurs during a shuffle phase.
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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.