Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
A data engineer needs to read a CSV file from cloud storage into a Spark DataFrame in Databricks. The file has a header row and uses commas as delimiters. The engineer wants to infer the schema automatically. Which code snippet correctly reads the file?
⚠ Common exam trap
The trap here is using the wrong method order or passing options as arguments to load instead of using the option method.
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
✓
spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("path/to/file.csv")
The correct method chain uses spark.read.format("csv") to specify the format, then .option("header", "true") and .option("inferSchema", "true") to set the options, and finally .load(path). This is the standard and correct way to read a CSV file with schema inference in Spark.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("path/to/file.csv")
Why this is correct
This correctly specifies the CSV format, sets header to true to use the first row as column names, and inferSchema to true to automatically detect data types. The load method reads the file. This is the standard way to read CSV with schema inference in Spark.
- ✗
spark.read.option("format", "csv").option("header", "true").option("inferSchema", "true").load("path/to/file.csv")
Why it's wrong here
The format is specified via option("format", "csv"), which is not the correct way to set the format. The format should be set using the format method. This would result in an error or incorrect behavior because the format option is not recognized. The correct approach is to use spark.read.format("csv").
- ✗
spark.read.csv("path/to/file.csv").option("header", "true").option("inferSchema", "true")
Why it's wrong here
The options are applied after the read.csv call, which returns a DataFrame. Options must be set before the load or on the DataFrameReader. This code would not apply the options to the read operation. It would attempt to call option on a DataFrame, which is not valid, causing an error.
- ✗
spark.read.format("csv").load("path/to/file.csv", header=True, inferSchema=True)
Why it's wrong here
The load method does not accept header and inferSchema as parameters. These must be set as options using the option method. This syntax would cause a TypeError because load only takes a path (and optionally more paths) but not these options. It is a common mistake to pass options as arguments to load.
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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-DE-Pro 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-DE-Pro exam.