Databricks-Spark-Assoc Using Spark Connect Practice Question
A developer has a local Python script that connects to a Databricks cluster using Spark Connect and creates a DataFrame from a small list of tuples. They then call .collect() on the DataFrame and receive the results. Which statement accurately describes how the data and operations are processed in this scenario?
⚠ Common exam trap
The trap here is assuming that Spark Connect runs a local Spark engine on the client, when in fact all computation is performed on the remote Spark server.
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 client serializes the logical plan and sends it to the Spark server, which executes the plan and returns the collected results.
Spark Connect uses a client-server architecture where the client builds a logical plan and sends it to the Spark server via gRPC. The server executes the plan on the cluster and returns the results. This decouples the client from the Spark driver, allowing remote execution without a local Spark context.
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 DataFrame is created and processed entirely on the client; the cluster is only used for storage.
Why it's wrong here
This is incorrect because Spark Connect follows a client-server architecture where the client sends logical plans to the server for execution. Creating a DataFrame from local data still results in a logical plan that is executed on the Spark server. The client does not process the DataFrame operations locally; it only builds the plan and collects results.
- ✓
The client serializes the logical plan and sends it to the Spark server, which executes the plan and returns the collected results.
Why this is correct
This is correct because Spark Connect decouples the client from the Spark driver. The client builds a logical plan for the DataFrame operations and sends it over gRPC to the Spark server, which executes the plan on the cluster. The results are then returned to the client when an action like collect() is called.
- ✗
The client executes the DataFrame operations locally using a built-in Spark engine, then syncs the results to the cluster.
Why it's wrong here
This is incorrect because Spark Connect does not run a local Spark engine on the client. The client only constructs logical plans and communicates with the remote Spark server. There is no local execution of DataFrame operations; all computation occurs on the cluster.
- ✗
The client sends the raw data to the cluster, which then returns a Python object that the client uses to perform further operations locally.
Why it's wrong here
This is incorrect because the client does not send raw data for local processing. Instead, it sends the logical plan representing the operations. The cluster executes the plan and returns only the final results when an action is triggered. The client does not perform further distributed operations locally.
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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.