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PySpark MCQs and Answers with Explanation: PySpark is the Python API for Apache Spark, a fast and scalable big data processing framework. It enables developers to write Spark applications using Python, providing a high-level API that simplifies the development process. PySpark supports a wide range of data sources and formats, making it a popular choice for big data processing and analysis. The following PySpark MCQs with Answers aims to test your knowledge of PySpark, the Python API for Apache Spark, a powerful big data processing framework.
PySpark MCQs with Answers
These PySpark Multiple Choice Questions/ PySpark MCQs cover various aspects of PySpark, including its features, functionality, and tools. PySpark is an essential library for developers looking to process and analyze large datasets efficiently. Test your understanding of PySpark by answering these Top 25 PySpark Quiz Questions to improve your knowledge of this powerful API.
PySpark Multiple Choice Questions
| Name | PySpark |
| Exam Type | MCQ (Multiple Choice Questions) |
| Category | Technical Quiz |
| Mode of Quiz | Online |
Top 25 PySpark MCQ Questions | PySpark Quiz
1. Which of the following statements is true about PySpark?
A) PySpark is a Python library used for Big Data processing.
B) PySpark is a standalone data processing system.
C) PySpark is used for processing data only in small batches.
D) PySpark does not support distributed processing.
Answer: A
Explanation: PySpark is a Python library used for Big Data processing. It is built on top of Apache Spark, which is a distributed computing system. PySpark provides APIs in Python for data processing, machine learning, and graph processing.
2. Which of the following is a transformation operation in PySpark?
A) count()
B) filter()
C) collect()
D) reduce()
Answer: B
Explanation: filter() is a transformation operation in PySpark. It creates a new RDD by selecting elements from an existing RDD based on a condition. Other transformation operations in PySpark include map(), flatMap(), union(), distinct(), and groupByKey().
3. Which of the following is an action operation in PySpark?
A) map()
B) filter()
C) count()
D) flatMap()
Answer: C
Explanation: count() is an action operation in PySpark. It returns the number of elements in an RDD. Other action operations in PySpark include collect(), reduce(), take(), and foreach().
4. Which of the following is used to create an RDD in PySpark?
A) DataFrame
B) DataSet
C) SQLContext
D) SparkContext
Answer: D
Explanation: SparkContext is used to create an RDD in PySpark. It is the entry point to the Spark computing system and provides APIs to create RDDs, accumulates values, and manipulate data. Other Spark components in PySpark include SQLContext, SparkSession, and DataFrameReader.
5. Which of the following is an advantage of using PySpark?
A) It is easy to learn and use.
B) It supports only batch processing.
C) It can only process structured data.
D) It is slower than other Big Data processing systems.
Answer: A
Explanation: One of the advantages of using PySpark is that it is easy to learn and use. PySpark provides a Python API for data processing, which is familiar to Python developers. PySpark also supports real-time processing, unstructured data processing, and machine learning.
6. Which of the following is a distributed data processing system?
A) Pandas
B) NumPy
C) PySpark
D) SciPy
Answer: C
Explanation: PySpark is a distributed data processing system. It is built on top of Apache Spark, which is a distributed computing system that can process large volumes of data in parallel across a cluster of computers.
7. Which of the following is used to read data from a file in PySpark?
A) readTextFile()
B) writeTextFile()
C) readDataFrame()
D) writeDataFrame()
Answer: A
Explanation: readTextFile() is used to read data from a file in PySpark. It reads the contents of a file and creates an RDD with each line of the file as an element. Other file input/output operations in PySpark include read.csv(), read.json(), write.csv(), and write.json().
8. Which of the following is used to convert an RDD to a DataFrame in PySpark?
A) toDataFrame()
B) createDataFrame()
C) RDDtoDF()
D) fromRDD()
Answer: B
Explanation: createDataFrame() is used to convert an RDD to a DataFrame in PySpark. It creates a DataFrame from an RDD with a specified schema. Other DataFrame operations in PySpark include select(), filter(), groupBy(), and join().
9. Which of the following is used to cache an RDD in memory in PySpark?
A) persist()
B) cache()
C) saveAsTextFile()
D) collect()
Answer: A
Explanation: persist() is used to cache an RDD in memory in PySpark. It stores the RDD in memory and/or on disk so that it can be reused efficiently in subsequent operations. Other RDD operations in PySpark include mapPartitions(), sortByKey(), reduceByKey(), and aggregateByKey().
10. Which of the following is a transformation operation that shuffles data in PySpark?
A) map()
B) filter()
C) groupByKey()
D) reduce()
Answer: C
Explanation: groupByKey() is a transformation operation that shuffles data in PySpark. It groups the values of each key in an RDD and creates a new RDD of (key, value) pairs. Other shuffling operations in PySpark include sortByKey(), reduceByKey(), and aggregateByKey().
11. Which of the following is used to create a PairRDD in PySpark?
A) map()
B) flatMap()
C) groupByKey()
D) zip()
Answer: D
Explanation: zip() is used to create a PairRDD in PySpark. It creates a new RDD by aggregating the elements of two RDDs into pairs. The first element of each RDD becomes the key, and the second element becomes the value. Other PairRDD operations in PySpark include reduceByKey(), groupByKey(), and join().
12. Which of the following is used to broadcast a read-only variable in PySpark?
A) sc.broadcast()
B) spark.broadcast()
C) rdd.broadcast()
D) broadcast()
Answer: A
Explanation: sc.broadcast() is used to broadcast a read-only variable in PySpark. It broadcasts the variable to all nodes in a Spark cluster so that it can be accessed efficiently by tasks. Other broadcasting operations in PySpark include accumulators and counters.
13. Which of the following is a built-in machine learning algorithm in PySpark?
A) Linear Regression
B) K-Means Clustering
C) Random Forest
D) All of the above
Answer: D
Explanation: PySpark provides several built-in machine learning algorithms, including Linear Regression, K-Means Clustering, Random Forest, Decision Trees, Gradient Boosting, and Naive Bayes. These algorithms can be used for regression, classification, clustering, and collaborative filtering.
14. Which of the following is a method to improve the performance of PySpark jobs?
A) Partitioning
B) Caching
C) Shuffling
D) None of the above
Answer: A
Explanation: Partitioning is a method to improve the performance of PySpark jobs. It involves dividing an RDD into smaller partitions, which can be processed in parallel across multiple nodes in a Spark cluster. Other methods to improve PySpark performance include caching, data serialization, and memory management.
15. Which of the following is a type of join operation in PySpark?
A) Inner Join
B) Outer Join
C) Left Join
D) All of the above
Answer: D
Explanation: PySpark supports several types of join operations, including Inner Join, Outer Join, Left Join, Right Join, and Full Join. Join operations are used to combine two RDDs based on a common key.
16. Which of the following is used to write data to a file in PySpark?
A) readTextFile()
B) writeTextFile()
C) readDataFrame()
D) writeDataFrame()
Answer: B
Explanation: writeTextFile() is used to write data to a file in PySpark. It writes the contents of an RDD to a file with each element of the RDD on a separate line. Other file input/output operations in PySpark include write.csv(), write.json(),
17. Which of the following is used to read data from a CSV file in PySpark?
A) readCSV()
B) readTextFile()
C) readJSON()
D) read.parquet()
Answer: A
Explanation: readCSV() is used to read data from a CSV file in PySpark. It reads the contents of a CSV file and creates a DataFrame with each row of the file as a separate row in the DataFrame. Other file input/output operations in PySpark include read.json(), read.parquet(), and read.text().
18. Which of the following is used to aggregate data in PySpark?
A) reduce()
B) aggregate()
C) groupByKey()
D) collect()
Answer: B
Explanation: aggregate() is used to aggregate data in PySpark. It applies a function to each partition of an RDD and then combines the results using another function. Other aggregation operations in PySpark include reduce(), fold(), and combineByKey().
19. Which of the following is used to sort data in PySpark?
A) sort()
B) sortByKey()
C) groupByKey()
D) reduceByKey()
Answer: B
Explanation: sortByKey() is used to sort data in PySpark. It sorts an RDD of (key, value) pairs by the key in ascending or descending order. Other sorting operations in PySpark include sort(), sortBy(), and sortByValue().
20. Which of the following is used to convert an RDD to a DataFrame in PySpark?
A) toDF()
B) toDataFrame()
C) asDF()
D) asDataFrame()
Answer: A
Explanation: toDF() is used to convert an RDD to a DataFrame in PySpark. It creates a DataFrame with columns named _1, _2, _3, etc. based on the number of elements in each row of the RDD. Other DataFrame operations in PySpark include select(), filter(), join(), and groupBy().
21. Which of the following is used to rename a column in a PySpark DataFrame?
A) withColumn()
B) renameColumn()
C) rename()
D) column()
Answer: C
Explanation: rename() is used to rename a column in a PySpark DataFrame. It renames the specified column to a new name. Other DataFrame operations in PySpark include withColumn(), withColumnRenamed(), and drop().
22. Which of the following is used to filter rows in a PySpark DataFrame?
A) select()
B) filter()
C) join()
D) groupBy()
Answer: B
Explanation: filter() is used to filter rows in a PySpark DataFrame. It selects the rows that satisfy a specified condition. Other DataFrame operations in PySpark include select(), join(), groupBy(), and orderBy().
23. Which of the following is used to aggregate data in a PySpark DataFrame?
A) groupBy()
B) join()
C) filter()
D) orderBy()
Answer: A
Explanation: groupBy() is used to aggregate data in a PySpark DataFrame. It groups the rows in the DataFrame based on one or more columns and applies an aggregation function to each group. Other DataFrame operations in PySpark include join(), filter(), and orderBy().
24. Which of the following is used to write data to a Parquet file in PySpark?
A) write.parquet()
B) write.csv()
C) write.json()
D) write.text()
Answer: A
Explanation: write.parquet() is used to write data to a Parquet file in PySpark. Parquet is a columnar storage format that is optimized for query performance. Other file input/output operations in PySpark include write.csv(), write.json(), and write.text().
25. Which of the following is used to cache a PySpark DataFrame in memory?
A) cache()
B) persist()
C) checkpoint()
D) repartition()
Answer: B
Explanation: persist() is used to cache a PySpark DataFrame in memory. It caches the DataFrame in memory or on disk so that subsequent actions can be performed more quickly. Other DataFrame operations in PySpark include cache(), unpersist(), checkpoint(), and repartition().
We hope that the PySpark MCQs with Answers provided by our Freshersnow team have helped you better understand the PySpark concept. By testing your knowledge with these PySpark MCQ Questions, you can further enhance your understanding of this powerful API and develop high-performance Spark applications using Python with ease.