Databricks-Spark-Assoc Spark Architecture and Components Practice Question
Which of the following correctly describes the relationship between a Spark Job and a Spark Stage?
⚠ Common exam trap
Students often confuse the hierarchy, mistakenly believing that a single stage contains multiple jobs or that tasks within a stage require network shuffles, overlooking that shuffle boundaries actually define stages.
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
✓
A stage is a set of parallel tasks that do not require a shuffle.
In Spark's execution architecture, a job is composed of multiple stages. Stages are defined by shuffle boundaries. When a job is submitted, the DAG scheduler breaks it into stages based on wide transformations that require data movement across the network. Understanding this hierarchy is essential for diagnosing performance issues, as it allows developers to identify which specific parts of their code lead to expensive shuffle operations, thus enabling better query optimization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A job is a single task that runs on one executor.
Why it's wrong here
A job is a much larger unit of work. It represents the top-level execution resulting from an action, such as count() or save(). A job can contain many stages, and each stage contains many tasks. Describing a job as a single task is fundamentally incorrect in Spark's architecture.
- ✓
A stage is a set of parallel tasks that do not require a shuffle.
Why this is correct
Stages are defined by shuffle boundaries. A single stage consists of a set of tasks that can be executed in parallel without any data exchange between them. Once data must be shuffled, a new stage is triggered, making this the correct definition of the relationship between tasks and stages.
- ✗
Stages are independent and never depend on each other.
Why it's wrong here
Stages in a job have dependencies; later stages often require the output of earlier stages. These dependencies are represented in the DAG. Saying they never depend on each other ignores the fundamental way Spark builds execution plans for complex queries involving multi-step data transformations and aggregations.
- ✗
A job must contain exactly one stage.
Why it's wrong here
Jobs can contain many stages depending on the complexity of the query. For example, a simple filter operation might be one stage, but a group-by and join operation will involve multiple stages separated by shuffle boundaries. A job is not limited to a single stage, as it can involve many.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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