MCQs on Spark Streaming | Apache Spark MCQs Questions

Apache Spark is a powerful tool for big data processing, and its streaming capabilities are widely used for real-time data processing. Chapter 5 focuses on Spark Streaming, exploring essential topics such as real-time data processing, DStream and Structured Streaming APIs, windowing operations, state management, fault tolerance, and integration with tools like Kafka and Flume. These Apache Spark MCQs Questions will help you enhance your understanding of Spark Streaming and its applications, making it easier to design efficient, fault-tolerant, and scalable streaming solutions.

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Real-Time Data Processing with Spark Streaming

1. What is the primary use case for Spark Streaming?
   a) Batch processing of static data
   b) Real-time processing of streaming data
   c) Data visualization
   d) Data storage
2. Which of the following best describes the nature of Spark Streaming?
   a) Stream processing in fixed intervals
   b) Asynchronous batch processing
   c) Continuous query processing
   d) Real-time predictive analytics
3. In Spark Streaming, what is a micro-batch?
   a) A small unit of memory in the cluster
   b) A fixed interval of streaming data processed as a batch
   c) A cached dataset in Spark
   d) A temporary storage location
4. Which of these operations can be performed in real-time using Spark Streaming?
   a) Filtering and transformation of data
   b) ETL processing
   c) Real-time analytics
   d) All of the above
5. How is data typically ingested into a Spark Streaming application?
   a) By reading from an RDD
   b) By using structured datasets
   c) By connecting to data sources like Kafka, Flume, or sockets
   d) By loading static files

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DStream and Structured Streaming API

6. What is a DStream in Spark Streaming?
   a) A distributed stream of data processed in real time
   b) A static dataset stored in memory
   c) A Java application for Spark jobs
   d) A command-line tool for monitoring
7. How is Structured Streaming different from DStreams?
   a) Structured Streaming processes static data only
   b) Structured Streaming provides a higher-level declarative API
   c) DStreams support real-time processing, while Structured Streaming does not
   d) DStreams are used for visualization
8. What is the default format of data processing in DStreams?
   a) JSON
   b) RDD
   c) DataFrame
   d) CSV
9. Which operation is supported by both DStream and Structured Streaming APIs?
   a) SQL-like querying
   b) Aggregations
   c) Windowing operations
   d) All of the above
10. How can you convert a DStream to a DataFrame in Spark Streaming?
    a) Using the toDF() method
    b) By using the map() transformation
    c) By applying a SQL query
    d) By saving it as a file

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Windowing Operations and State Management

11. What is the purpose of windowing in Spark Streaming?
    a) To store data for a long-term process
    b) To apply operations over a sliding time window
    c) To transform data into key-value pairs
    d) To write results to a database
12. Which function is used to define window duration in Spark Streaming?
    a) reduceByKey()
    b) updateStateByKey()
    c) window()
    d) filter()
13. How does state management work in Spark Streaming?
    a) By maintaining a static snapshot of data
    b) By tracking the cumulative state of streaming data
    c) By storing data in an external database
    d) By using caching mechanisms
14. Which of these operations involves maintaining state in Spark Streaming?
    a) Stateful transformations
    b) Stateless computations
    c) Micro-batch processing
    d) Data serialization
15. What is the default duration for a sliding window in Spark Streaming?
    a) 10 seconds
    b) 1 minute
    c) It depends on the user-defined configuration
    d) 5 minutes

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Fault Tolerance in Streaming Applications

16. How does Spark Streaming achieve fault tolerance?
    a) By replicating data to multiple nodes
    b) By using the Write Ahead Log (WAL)
    c) By creating backups of input data
    d) By running redundant jobs
17. What happens if a worker node fails in a Spark Streaming application?
    a) The streaming application stops processing
    b) The data is reprocessed from the last checkpoint
    c) The job is terminated
    d) Data processing is skipped
18. What is the purpose of checkpointing in Spark Streaming?
    a) To visualize streaming data
    b) To recover from failures and maintain state
    c) To schedule Spark jobs
    d) To optimize memory usage
19. Which type of data can be checkpointed in Spark Streaming?
    a) RDDs
    b) Streaming logs
    c) Driver configurations
    d) None of the above
20. What type of checkpointing is required to recover streaming state?
    a) Metadata checkpointing
    b) Directory checkpointing
    c) Stateful checkpointing
    d) None

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Integration with Kafka and Flume

21. What is Kafka commonly used for in Spark Streaming?
    a) Batch processing
    b) Real-time data ingestion
    c) Storing static datasets
    d) Data visualization
22. Which API does Spark Streaming provide for integration with Kafka?
    a) KafkaUtils
    b) SparkKafkaConnector
    c) KafkaIntegration
    d) KafkaStreams
23. What is Flume in the context of Spark Streaming?
    a) A streaming SQL engine
    b) A service for collecting and transferring log data
    c) A cloud storage service
    d) A batch processing tool
24. Which operation is essential for consuming data from Kafka in Spark Streaming?
    a) createStream()
    b) consumeFromKafka()
    c) readFromSocket()
    d) createKafkaStream()
25. How can Spark Streaming process data from Kafka topics?
    a) By using DStreams to subscribe to topics
    b) By writing custom input formats
    c) By using SQL queries directly
    d) By loading data files from Kafka
26. Which type of messaging system is Kafka categorized as?
    a) Pub-sub messaging system
    b) Relational database system
    c) ETL tool
    d) Data visualization platform
27. What is the role of Flume in Spark Streaming integration?
    a) To process static datasets
    b) To aggregate and transfer streaming data
    c) To query large databases
    d) To manage cluster resources
28. Which of the following is required to connect Spark Streaming to Kafka?
    a) Kafka broker information
    b) Hive configuration
    c) HDFS URI
    d) MySQL connector
29. What is a key benefit of integrating Spark Streaming with Kafka?
    a) Real-time data ingestion and processing
    b) Enhanced visualization of data
    c) Automatic batch scheduling
    d) Improved storage optimization
30. What kind of data does Flume typically handle in Spark Streaming?
    a) Transactional data
    b) Log and event data
    c) Structured data
    d) Statistical summaries

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Answers Table

Qno	Answer (Option with the text)
1	b) Real-time processing of streaming data
2	a) Stream processing in fixed intervals
3	b) A fixed interval of streaming data processed as a batch
4	d) All of the above
5	c) By connecting to data sources like Kafka, Flume, or sockets
6	a) A distributed stream of data processed in real time
7	b) Structured Streaming provides a higher-level declarative API
8	b) RDD
9	d) All of the above
10	a) Using the toDF() method
11	b) To apply operations over a sliding time window
12	c) window()
13	b) By tracking the cumulative state of streaming data
14	a) Stateful transformations
15	c) It depends on the user-defined configuration
16	b) By using the Write Ahead Log (WAL)
17	b) The data is reprocessed from the last checkpoint
18	b) To recover from failures and maintain state
19	a) RDDs
20	c) Stateful checkpointing
21	b) Real-time data ingestion
22	a) KafkaUtils
23	b) A service for collecting and transferring log data
24	d) createKafkaStream()
25	a) By using DStreams to subscribe to topics
26	a) Pub-sub messaging system
27	b) To aggregate and transfer streaming data
28	a) Kafka broker information
29	a) Real-time data ingestion and processing
30	b) Log and event data