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

1. A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A) Increase the memory allocated to the Spark Driver.
B) Optimize the data processing logic by repartitioning the DataFrame.
C) Cache large DataFrames to persist them in memory.
D) Modify the Spark configuration to disable garbage collection

2. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

A) Worker Nodes
B) CPU Cores
C) Driver Nodes
D) Executors

3. 41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?

A) df1.orderBy("Name", "count")
B) df1.sort("Name", "count")
C) df1.orderBy(col("count").desc(), col("Name").asc())
D) df1.orderBy(col("Name").desc(), col("count").asc())

4. 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, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
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 program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
D) In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs

5. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
B) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
C) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
D) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.

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