Databricks-Spark-Assoc Spark Architecture and Components Practice Question
What happens when an action is called on a Spark DataFrame?
⚠ Common exam trap
Candidates often confuse transformations with actions, mistakenly believing that operations like select(), filter(), or withColumn() trigger immediate computation in Spark when they actually just build up the logical plan.
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 entire transformation graph is executed.
An action triggers the execution of the DAG. Spark creates a job, splits it into stages, and launches tasks on executors to process the data. Until an action is called, Spark only builds a logical plan (lazy evaluation). This is fundamental for query optimization; it allows the Catalyst Optimizer to analyze the entire plan before execution, ensuring that unnecessary operations are pruned and the most efficient physical plan is generated for 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.
- ✗
The data is immediately written to disk.
Why it's wrong here
Data is only written to disk if the action is explicitly an output operation like save(). Standard actions like count() or collect() do not perform disk writes. Assuming all actions are persistent writes leads to misunderstandings about Spark's memory-first, transformation-based architecture and how it manages data flow efficiently.
- ✓
The entire transformation graph is executed.
Why this is correct
Actions are the only operations that force Spark to evaluate the lazy transformation chain. By triggering the DAG scheduler, Spark processes the data and returns a result to the driver or writes it to a sink. This execution model allows for significant query-level optimizations before runtime starts.
- ✗
The Spark context is shut down.
Why it's wrong here
Calling an action does not stop the Spark context. The session remains active to allow for further queries and transformations. Shutting down the context would force a complete reload of data and metadata, making iterative development and interactive analysis impossible in a distributed Spark environment.
- ✗
The cache is automatically cleared.
Why it's wrong here
Actions do not influence the cache state unless explicitly instructed to unpersist data. Caching is a deliberate user decision to keep datasets in memory. Automatic cache clearing would destroy the performance benefits of caching, which is meant to persist through multiple actions and stages during a long-running job.
About these practice questions
Courseiva writes every Databricks-Spark-Assoc question from scratch — 295 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.