Authentic Databricks Certification Exam Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Questions Answers

Option B is the correct and idiomatic approach in PySpark to split a string column (like email) based on a delimiter such as "@".

The split(col("email"), "@") function returns an array with two elements: username and domain.

getItem(0) retrieves the first part (username).

getItem(1) retrieves the second part (domain).

withColumn() is used to create new columns from the extracted values.

Example from official Databricks Spark documentation on splitting columns:

from pyspark.sql.functions import split, col

df.withColumn("username", split(col("email"), "@").getItem(0)) \

withColumn("domain", split(col("email"), "@").getItem(1))

⚠️ Why other options are incorrect:

A uses fixed substring indices (substr(0, 5)), which won’t correctly extract usernames and domains of varying lengths.

C uses substring_index, which is available but less idiomatic for splitting emails and is slightly less readable.

D removes "@" from the email entirely, losing the separation between username and domain, and ends up duplicating values in both fields.

Therefore, Option B is the most accurate and reliable solution according to Apache Spark 3.5 best practices.

The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such as reduce() or aggregate().

From the documentation:

“To perform global aggregations on distributed data, actions like reduce() are commonly used to collect summaries such as min/max/avg.”

Accumulators (Option B) do not support max operations directly and are not intended for such analytics.

Broadcast (Option C) is used to send data to workers, not collect from them.

Spark UI (Option D) is a monitoring tool — not an analytics collection interface.

Final Answer: A

The correct way to overwrite an existing file using the DataFrameWriter is:

df.write.mode("overwrite").json("path/to/file")

Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.