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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The conversion will automatically distribute the data across worker nodes
B) The operation will load all data into the driver's memory, potentially causing memory overflow
C) The operation will fail if the Pandas DataFrame exceeds 1000 rows
D) Data will be lost during conversion

2. A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

A) filtered_df = users_raw_df.na.drop(how='any')
B) filtered_df = users_raw_df.na.drop(how='all', thresh=None)
C) filtered_df = users_raw_df.na.drop(how='all')
D) filtered_df = users_raw_df.na.drop(thresh=0)

3. A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A) It is not able to handle deduplication in this scenario
B) It accepts watermarks in seconds and the code results in an error
C) It removes all duplicates regardless of when they arrive
D) It removes duplicates that arrive within the 30-minute window specified by the watermark

4. 30 of 55.
A data engineer is working on a num_df DataFrame and has a Python UDF defined as:
def cube_func(val):
return val * val * val
Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?

A) spark.udf.register("cube_func", cube_func)
num_df.selectExpr("cube_func(num)").show()
B) spark.createDataFrame(cube_func("num")).show()
C) num_df.select(cube_func("num")).show()
D) num_df.register("cube_func").select("num").show()

5. An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A) It filters the id values before performing the join.
B) It ensures that the join happens only when the id values are identical.
C) It increases the partition size for df1 and df2.
D) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.

Solutions: