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

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

A) 20
B) 10
C) 1
D) Same number as the cluster executors

2. A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

A) Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
B) Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges
C) Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
D) Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy

3. 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) filter
C) select
D) groupBy

4. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A) By configuring the option recoveryLocation during writeStream
B) By configuring the option checkpointLocation during writeStream
C) By configuring the option checkpointLocation during readStream
D) By configuring the option recoveryLocation during the SparkSession initialization

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

Solutions: