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PySpark Practice Test Questions
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PySpark is the Python API for Apache Spark. It enables you to perform real-time, large-scale data processing in a distributed environment using Python. It also provides a PySpark shell for interactively analyzing your data.
 

Apache Spark is an open-source, cluster computing system which is used for big data solutions.

PySpark Practice Test Questions
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25 Questions

1. The batching can be disabled by setting it to ____.
2. An integrated ____ programming API is provided by PySpark SQL in Spark.
3. When working with ____, Python's dynamic typing comes in handy.
4. A UDF extends Spark SQL's DSL vocabulary for transforming DataFrames by defining a new ____-based function.
5. The Apache Spark framework can perform a variety of tasks, such as ____, running Machine Learning algorithms, or working with graphs or streams.
6. ___ operations are carried out on the accumulator variables to combine the information.
7. A ____ program is written in Object-Oriented Programming (OOP).
8. The active stage ids are returned by ____ in an array.
9. Missing data can be handled via ____.
10. As part of Netflix's real-time processing, ____ is used to make an online movie or web series more personalized for customers based on their interests.
11. Using ____, PySpark allows you to upload your files.
12. What is/are the drawback(s) of Hive?
13. The ____ directory contains the Spark installation files.
14. Serializing another function can be done using the ____ function.
15. Java version 1.8.0 or higher is required for PySpark, as is ____ version 3.6 or higher.
16. Using Spark, users can implement big data solutions in an ____-source, cluster computing environment.
17. Among the method(s) that need to be defined by the custom profiler is/are:.
18. DataFrame and SQL functionality is accessed through ____.
19. Rather than shipping a copy of a variable with each task, broadcast lets the programmer store a ____-only variable locally.
20. In PySpark, ____ library is provided, which makes integrating Python with Apache Spark easy.
21. ____ are among the key features of Apache Spark. It is easy to use, provides simplicity, and can run virtually anywhere.
22. Job and stage progress can be monitored using PySpark's ___-level APIs.
23. Using Spark____, we can set some parameters and configurations to run a Spark application on a local cluster or dataset.
24. In-memory processing of large data makes PySpark ideal for ____ computation.
25. Which of the following is/are the feature(s) of the SparkConf?

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