Answer-first summary for fast verification
Answer: ```python storesDF.printSchema(True) schema_fields = [f.name for f in storesDF.schema.fields] print("location" in schema_fields) ```
**Correct Answer: D** * **`printSchema(True)`**: By default, `printSchema()` shows the structure. To see the extended **metadata** associated with columns (often used in ML or Spark SQL), you must pass `True`. * **`.schema` vs `.printSchema()`**: This is a common exam trick. `printSchema()` is a **method** that returns `None`; you cannot extract data from it. `schema` is an **attribute** that returns a `StructType` object, which has a `.fields` property you can iterate over. **Why others are wrong:** * **Option A** incorrectly tries to call `.schema` like a function. * **Option B** uses the wrong argument type for the metadata display and a non-existent method `getNames()`. * **Option C** fails to include the metadata argument and treats `schema` as a standalone function rather than a DataFrame property.
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You are working with a Spark DataFrame called storesDF read from a nested JSON file. Your task is to:
Which of the following code blocks correctly performs these tasks?
A
storesDF.schema(True)
schema_fields = [f.name for f in storesDF.printSchema.fields]
print("location" in schema_fields)
storesDF.schema(True)
schema_fields = [f.name for f in storesDF.printSchema.fields]
print("location" in schema_fields)
B
storesDF.schema("True")
schema_fields = [f.name for f in storesDF.printSchema.fields]
storesDF.schema("True")
schema_fields = [f.name for f in storesDF.printSchema.fields]
C
schema.printSchema(True)
schema_fields = [f.name for f in storesDF.schema.fields]
schema.printSchema(True)
schema_fields = [f.name for f in storesDF.schema.fields]
D
storesDF.printSchema(True)
schema_fields = [f.name for f in storesDF.schema.fields]
print("location" in schema_fields)
storesDF.printSchema(True)
schema_fields = [f.name for f in storesDF.schema.fields]
print("location" in schema_fields)
