Which of the following code blocks reads a CSV at the file path filePath into a Data Frame with the specified schema schema?

Question

Options

• Aspark.read().csv(filePath)
• Bspark.read().schema("schema").csv(filePath)
• Cspark.read.schema(schema).csv(filePath)
• Dspark.read.schema("schema").csv(filePath)
• Espark.read().schema(schema).csv(filePath)

• B
  2% (1)
• C
  94% (44)
• E
  4% (2)

Explanation

Option C is correct because in PySpark, spark.read is a property (a DataFrameReader object), not a method - so it takes no parentheses. The .schema() method accepts a StructType variable (here schema), not a string, followed by .csv(filePath) to specify the source format and path.

Why the distractors fail:

• A & B & E: spark.read() with parentheses is a TypeError - read is a property, not callable.
• B & D: Passing "schema" (a string literal) to .schema() instead of the actual StructType variable schema will either fail or be misinterpreted.
• A: Also missing .schema() entirely, so no schema is applied at all.

Memory tip: Think "read is free, schema needs the variable" - spark.read has no (), and .schema() wants the real object, not its name in quotes.

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