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

1. 31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?

A) 10
B) 5
C) 20
D) Same number as the cluster executors

2. A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A) DataFrame.filter()
B) DataFrame.groupBy().agg()
C) DataFrame.select()
D) DataFrame.withColumn()

3. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Recreate the users_vw and query the data using Spark
B) Save the users_vw definition and query using Spark
C) Query the users_vw using Spark
D) Persist the users_vw data as a table

4. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Debuggability: the ability to perform interactive debugging directly from the application code
B) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs
C) Stability: isolation of application code and dependencies from each other and the Spark driver
D) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself

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

Solutions: