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

1. A Data Analyst is working on the DataFramesensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits therecordcolumn into separate columns and has one array item per row?
A)

B)

C)

D)

A) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
B) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
C) exploded_df = exploded_df.select("record_datetime", "record_exploded")
D) exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")

2. An engineer wants to join two DataFramesdf1anddf2on the respectiveemployee_idandemp_idcolumns:
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 fails to execute because the column names employee_id and emp_id do not match automatically
B) The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
C) The code fails to execute because it must use on='employee_id' to specify the join column explicitly
D) The code fails to execute because PySpark does not support joining DataFrames with a different structure

3. A data engineer is working on the DataFrame:

(Referring to the table image: it has columnsId,Name,count, andtimestamp.) Which code fragment should the engineer use to extract the unique values in theNamecolumn into an alphabetically ordered list?

A) df.select("Name").orderBy(df["Name"].asc())
B) df.select("Name").distinct().orderBy(df["Name"])
C) df.select("Name").distinct()
D) df.select("Name").distinct().orderBy(df["Name"].desc())

4. In the code block below,aggDFcontains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) replace
B) aggregate
C) complete
D) append

5. A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A) In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
B) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
C) In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
D) In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.

Solutions: