Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps : Databricks Certified Associate Developer for Apache Spark 3.5 – Python

To view the schema of a Parquet file, you must use the DataFrameReader to load the Parquet data and call the .printSchema() method.

Correct syntax:

spark.read.parquet("events.parquet").printSchema()

This command loads the file metadata (without triggering a full read) and prints the column names, data types, and nullability information in a tree format.

Why the other options are incorrect:

A/D: SQL queries can’t directly introspect file schemas.

B: .show() displays data rows, not schema.

When you convert a large pyspark.pandas (aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using .toPandas(), Spark collects all partitions to the driver.

From the Spark documentation:

“Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver’s memory.”

Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.

Final Answer: D

To handle JSON files with evolving or differing schemas, Spark SQL supports the option mergeSchema 'true', which merges all fields across files into a unified schema.

Correct syntax:

CREATE EXTERNAL TABLE users

USING json

OPTIONS (path '/data/input.json', mergeSchema 'true');

This creates an external table directly on the JSON data, inferring schema automatically and merging variations.

Why the other options are incorrect:

B: Missing schema merge configuration — fails with inconsistent files.

C: inferSchema applies to CSV/other file types, not JSON.

D: mergeAll is not a valid Spark SQL option.