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

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

2. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) [Row(name='bambi'), Row(name='alladin', age=20)]
B) [Row(name='alladin', age=20)]
C) The code throws an error due to a schema mismatch.
D) [Row(name='bambi', age=None), Row(name='alladin', age=20)]

3. 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 program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
C) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
D) In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.

4. 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 inevent_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 duplicates that arrive within the 30-minute window specified by the watermark
D) It removes all duplicates regardless of when they arrive

5. What is the behavior for functiondate_sub(start, days)if a negative value is passed into thedaysparameter?

A) An error message of an invalid parameter will be returned
B) The number of days specified will be added to the start date
C) The same start date will be returned
D) The number of days specified will be removed from the start date

Solutions: