PDF Version DemoDatabricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
1. 43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?
A) Improved job execution speed due to reduced logging overhead
B) Prevention of driver log accumulation during long-running jobs
C) Enhanced executor performance due to reduced log size
D) Loss of access to past job logs and reduced debugging capability for completed jobs
2. 32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?
A) Avoid using a SparkSession and rely on SparkContext only.
B) Use a single SparkSession instance for the entire application.
C) Create a new SparkSession instance before each transformation.
D) Stop and restart the SparkSession after each action.
3. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?
A) It is available only with Python, thereby reducing the learning curve.
B) It computes results immediately using eager execution.
C) It runs on a single node only, utilizing memory efficiently.
D) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
4. 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.filter("count < 30")
B) streaming_df.count()
C) streaming_df.show()
D) streaming_df.select(countDistinct("name"))
5. 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 checkpointLocation during readStream
B) By configuring the option recoveryLocation during the SparkSession initialization
C) By configuring the option checkpointLocation during writeStream
D) By configuring the option recoveryLocation during writeStream
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: C |
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