Databricks-Spark-Assoc Using Spark Connect Practice Question
A developer is using Spark Connect to connect to a Databricks cluster. They attempt to read a CSV file from a path that exists on the client machine's local disk. The operation fails with a file not found error. What is the most likely reason for this failure?
⚠ Common exam trap
The trap here is assuming that the client and server share a file system, which is not the case in Spark Connect's decoupled architecture.
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 file path is interpreted relative to the server's file system, not the client's, so the server cannot find the file.
Spark Connect operates in a client-server model where the server executes all data operations. File paths are resolved on the server's file system. If a developer refers to a local file on the client machine, the server cannot access it, causing a file not found error. The correct approach is to place data in a location accessible to the cluster, such as cloud storage.
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 CSV reader requires a schema to be explicitly provided when using Spark Connect.
Why it's wrong here
Providing a schema is optional for CSV reading in Spark Connect, just as in classic Spark. If no schema is provided, Spark will infer it, though this may require an extra pass over the data. The failure is not due to a missing schema but because the file is not accessible from the server. Schema inference works fine when the file is reachable.
- ✓
The file path is interpreted relative to the server's file system, not the client's, so the server cannot find the file.
Why this is correct
In Spark Connect, all data access is performed by the server. When you specify a path, it is resolved on the server's file system, not the client's. If the file exists only on the client's local disk, the server cannot read it, resulting in a file not found error. The file must be accessible from the cluster, such as in cloud storage or a mounted volume.
- ✗
Spark Connect does not support reading CSV files; only Parquet and Delta are supported.
Why it's wrong here
Spark Connect supports reading CSV files through the same DataFrameReader API as classic Spark. The failure is not due to file format support. The issue is that the file path is local to the client, which the server cannot access. CSV reading is fully supported when the file is on a distributed file system or cloud storage accessible by the cluster.
- ✗
The client must first upload the CSV file to the server's local file system using spark.uploadFile().
Why it's wrong here
There is no spark.uploadFile() method in Spark Connect. While Spark Connect allows adding artifacts like JARs or Python files via addArtifacts, there is no generic file upload for data files. Data files should be placed in cloud storage or a distributed file system that the server can access. This option describes a non-existent method.
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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.