Databricks-Spark-Assoc Using Spark Connect Practice Question
A developer is building a Python application that connects to a Databricks cluster using Spark Connect. The application uses the `databricks-connect` package and is configured with the cluster ID and authentication credentials. During a test run, the developer calls `spark.sql("SELECT * FROM sales")` and then `df.show()`. What happens when the `show()` action is executed?
⚠ Common exam trap
The trap here is assuming that Spark Connect executes queries locally or downloads data, when it actually delegates execution to the remote 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 sends the logical plan to the Spark Connect server, which executes it on the cluster and returns the result rows back to the client.
In Spark Connect, the client builds a logical plan and sends it to the Spark Connect server via gRPC. The server executes the plan on the cluster and returns results. The client does not download data or execute locally. This architecture decouples the client from the driver, enabling remote execution and improved stability.
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 client uses a local SparkContext to connect to the cluster's driver and executes the query as if it were local.
Why it's wrong here
Spark Connect deliberately avoids using a local SparkContext. The client does not instantiate a SparkContext or connect directly to the driver. Instead, it communicates with the Spark Connect server, which manages the SparkSession on the cluster. This option reflects the legacy Spark client model, not Spark Connect.
- ✗
The client downloads the entire `sales` table to the local machine and executes the SQL query using a local Spark session.
Why it's wrong here
Spark Connect does not download data for local execution. The client only sends the query plan to the server, and all data processing occurs remotely on the cluster. Downloading the table would be inefficient and defeat the purpose of remote execution. This option misrepresents how Spark Connect handles DataFrame operations.
- ✓
The client sends the logical plan to the Spark Connect server, which executes it on the cluster and returns the result rows back to the client.
Why this is correct
Spark Connect uses a client-server architecture where the client builds an unresolved logical plan and sends it via gRPC to the Spark Connect server running on the cluster. The server optimizes and executes the plan, then streams results back to the client. This decouples the client from the driver, enabling remote execution and improved stability.
- ✗
The client compiles the SQL query into a JVM bytecode and sends it to the cluster for execution.
Why it's wrong here
Spark Connect does not compile queries into JVM bytecode on the client. Instead, it serializes the logical plan using Protocol Buffers and sends it over gRPC. The server handles query compilation and execution. This option incorrectly describes the communication mechanism and the role of the client.
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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.