Databricks-DA-Assoc Importing Data Practice Question
Exhibit
Error: AnalysisException: Path does not exist: dbfs:/mnt/data/sales_2023.csv
Refer to the exhibit. An analyst is attempting to read a CSV file using Spark. Why is the code failing?
⚠ Common exam trap
Candidates frequently assume the error is related to file permissions or Spark syntax, ignoring the most common issue: a simple typo or incorrect path string in the file system mount point.
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 provided does not exist in the mounted location.
This error occurs because the specified path in DBFS does not resolve to an actual file. This often happens if the mount point was not created correctly or if the file name was mistyped. Debugging file paths is a fundamental skill for analysts, as it involves verifying storage connectivity and ensuring the file system abstraction layer correctly points to the underlying cloud storage container where the data resides.
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 cluster is running on a single-node configuration.
Why it's wrong here
A single-node cluster is fully capable of accessing DBFS and external mounts. The error message explicitly points to a path issue, not a cluster architecture limitation. If the path were correct, the single node would be able to read the file successfully regardless of the compute cluster configuration.
- ✓
The file path provided does not exist in the mounted location.
Why this is correct
The AnalysisException clearly states that the path does not exist. This indicates that either the mount point /mnt/data is not correctly established or the specific file sales_2023.csv is missing or misspelled in the underlying storage. The analyst must verify the mount point and the file existence.
- ✗
Spark does not support reading CSV files directly from DBFS.
Why it's wrong here
Spark has robust support for reading CSV files from various sources including DBFS. The 'format("csv")' method is a core part of the Spark DataFrame API. The error is strictly related to path resolution, not a lack of functionality or capability regarding the file format being read by Spark.
- ✗
The user lacks sufficient memory to read the file.
Why it's wrong here
A memory issue would result in an OutOfMemoryError or a driver failure during execution, not a path-based AnalysisException. The path error is a metadata or file system level issue, meaning Spark has not even attempted to load the content of the file into the cluster memory yet.
Visual reference
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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-DA-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-DA-Assoc exam.