Databricks-Spark-Assoc Spark Architecture and Components Practice Question
What is the consequence of having 'wide dependencies' in a Spark job regarding the Spark Architecture?
⚠ Common exam trap
Candidates often assume wide dependencies are always bad and should be avoided at all costs. They miss the fact that they are sometimes necessary for operations like joins and aggregations.
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 forces the DAG scheduler to create a new stage boundary.
Wide dependencies occur when a partition in the parent RDD contributes to multiple partitions in the child RDD, necessitating a shuffle. This forces a boundary in the DAG, triggering the creation of a new stage. Understanding this is essential because every shuffle introduces network I/O, disk I/O, and serialization overhead, which are the primary performance costs that developers must minimize when designing efficient Spark transformations and data models.
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 allows for task pipelining within a single stage.
Why it's wrong here
Pipelining is only possible with narrow dependencies, where data can be processed without moving it between partitions. Wide dependencies break the pipeline because the shuffle requires data to be materialized and redistributed across the network before the next set of operations can begin on the worker nodes.
- ✓
It forces the DAG scheduler to create a new stage boundary.
Why this is correct
Because wide dependencies involve shuffles, the DAG scheduler must finish all parent tasks before starting the child stage. This mandatory barrier allows Spark to guarantee that all intermediate data is successfully written and available to the next set of tasks across the cluster's network nodes.
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It enables data locality optimizations automatically.
Why it's wrong here
Data locality is generally lost during a shuffle because data is redistributed across nodes based on keys. While Spark tries to place tasks where data lives, a wide dependency reshuffles data to new locations, often negating the benefits of the original data placement strategy used in the initial stage.
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It reduces the total number of tasks in the job.
Why it's wrong here
Wide dependencies often increase the number of tasks, especially if the shuffle results in a different number of partitions than the input. They add complexity and overhead rather than simplifying the execution graph. More tasks mean more scheduling overhead, which can degrade job performance if not properly partitioned.
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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.