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

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

1. An engineer wants to join two DataFrames df1 and df2 on the respective employee_id and emp_id columns:
df1: employee_id INT, name STRING
df2: emp_id INT, department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?

A) The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
B) The code fails to execute because it must use on='employee_id' to specify the join column explicitly
C) The code fails to execute because PySpark does not support joining DataFrames with a different structure
D) The code fails to execute because the column names employee_id and emp_id do not match automatically

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

3. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A) streaming_df.select(countDistinct("name"))
B) streaming_df.count()
C) streaming_df.show()
D) streaming_df.filter("count < 30")

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

5. A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A) coalesce
B) groupBy
C) select
D) filter

Solutions: