MCQs on Advanced Topics and Best Practices | Apache Spark MCQs Questions

If you’re preparing for certification exams or interviews related to Apache Spark, these Apache Spark MCQs Questions will help you dive deep into advanced topics and best practices. This set of questions covers important concepts such as Delta Lake, Lakehouse Architecture, Structured Streaming, security features in Spark, and more. Each question will give you a clearer understanding of key advanced features that are crucial for data engineers and developers working with big data processing and analytics using Apache Spark. Enhance your knowledge and prepare for real-world challenges by testing yourself with these detailed MCQs.

MCQs

1. Delta Lake and Lakehouse Architecture

1. What is the primary feature of Delta Lake?
   a) Schema evolution and ACID transactions
   b) Real-time analytics
   c) Data visualization
   d) Data replication
2. Delta Lake is based on which of the following technologies?
   a) Apache Kafka
   b) Apache Flink
   c) Apache Spark
   d) Apache Hadoop
3. Which of the following is a key benefit of Lakehouse architecture?
   a) Combining data lakes and data warehouses
   b) Increased storage costs
   c) Lower data processing speed
   d) Lack of integration with SQL
4. In Delta Lake, what operation is used to insert, update, or delete data?
   a) INSERT INTO
   b) MERGE INTO
   c) SELECT
   d) UPDATE
5. Which file format does Delta Lake primarily use?
   a) Avro
   b) Parquet
   c) ORC
   d) JSON
6. What is the role of the Delta log in Delta Lake?
   a) Storing metadata
   b) Storing raw data
   c) Handling user permissions
   d) Managing replication

2. Structured Streaming Advanced Features

7. Which method is used to define a streaming DataFrame in Spark Structured Streaming?
   a) spark.readStream
   b) spark.readStreamStream
   c) spark.streaming
   d) spark.sqlStream
8. What is a checkpoint in Structured Streaming?
   a) A method to store intermediate results
   b) A function to aggregate data
   c) A tool to enhance data visualization
   d) A mechanism for fault tolerance and recovery
9. How does Spark Structured Streaming handle late data?
   a) It discards late data
   b) It uses watermarking to handle late data
   c) It ignores time windows
   d) It buffers late data until the next batch
10. Which feature in Structured Streaming helps to handle event time processing?
    a) Watermarking
    b) Join operations
    c) Caching
    d) Windowing
11. Which mode allows Spark Structured Streaming to process data continuously as it arrives?
    a) Streaming mode
    b) Micro-batch mode
    c) Batch mode
    d) Real-time mode
12. What does the foreachBatch function in Structured Streaming allow you to do?
    a) Perform a batch operation on the data
    b) Apply transformations on each micro-batch
    c) Save results to an external system after each batch
    d) Both b and c

3. Writing Custom Input and Output Data Sources

13. Which of the following is required to write a custom data source in Spark?
    a) Implementing the InputFormat and OutputFormat
    b) Using only Parquet format
    c) Accessing only JDBC sources
    d) Utilizing the spark-submit command
14. To create a custom output data source, what Spark interface must be implemented?
    a) DataSourceV1
    b) DataSourceV2
    c) SQLContext
    d) RDD
15. What is the purpose of DataSourceV2 in Apache Spark?
    a) To provide support for custom input and output sources
    b) To improve query optimization
    c) To manage Spark jobs
    d) To handle real-time data processing
16. When writing custom input formats in Spark, which of the following is essential?
    a) Defining a schema for the data
    b) Writing custom SQL queries
    c) Using only structured data
    d) Implementing a memory caching strategy
17. How can you extend Spark to support new file formats?
    a) By using custom input and output data sources
    b) By modifying the core Spark engine
    c) By adding support for Hive
    d) By extending the Spark SQL module
18. What is the key advantage of writing custom data sources in Spark?
    a) Reduces data transfer times
    b) Allows integration with external data sources
    c) Improves the performance of joins
    d) Limits the types of data that can be processed

4. Security in Spark: Authentication and Encryption

19. What is the default authentication method for Spark?
    a) OAuth2
    b) Kerberos
    c) SSL
    d) Basic authentication
20. How can you enable SSL encryption in Spark?
    a) By setting spark.ssl.enabled to true
    b) By using Kerberos authentication
    c) By enabling encryption in the underlying file system
    d) By setting spark.sql.encryption
21. What is the role of Kerberos authentication in Spark security?
    a) Provides secure data transfer
    b) Verifies user identities and permissions
    c) Encrypts all Spark jobs
    d) Enables real-time security auditing
22. How does Spark handle encrypted data in transit?
    a) By using SSL/TLS encryption
    b) By using Hadoop’s encryption tools
    c) By disabling encryption during communication
    d) By compressing the data
23. Which of the following is a recommended security practice when working with Spark?
    a) Always use unencrypted connections
    b) Use Kerberos authentication for secure communication
    c) Disable access control for flexibility
    d) Avoid setting any security-related configurations
24. Which method can be used to secure access to sensitive data in Spark?
    a) Using AWS IAM roles
    b) Data masking
    c) Role-based access control (RBAC)
    d) All of the above

5. Future Trends and Contributions to Apache Spark

25. Which upcoming feature in Spark focuses on improving performance for real-time analytics?
    a) Photon Engine
    b) Data Lakehouse
    c) Structured Streaming v3
    d) Spark GraphX
26. What is the significance of the Project Tungsten in Apache Spark?
    a) Provides better support for distributed ML algorithms
    b) Improves the execution engine for optimized performance
    c) Adds advanced capabilities for real-time analytics
    d) Extends the SQL functionality in Spark
27. What is Project Lion in Apache Spark aimed at improving?
    a) Streamlining the integration with Hadoop
    b) Simplifying the use of Spark SQL
    c) Enhancing machine learning workflows
    d) Improving the performance of DataFrame operations
28. How does Project Blaze contribute to Apache Spark’s future development?
    a) By offering deeper integration with AWS S3
    b) By improving the query optimization capabilities
    c) By reducing the need for custom connectors
    d) By enhancing SQL capabilities in Spark
29. Which new API is being developed to enable faster query execution in Spark?
    a) DataFrames API v2
    b) Tungsten API
    c) Adaptive Query Execution (AQE)
    d) Apache Flink API
30. How can contributions to the Apache Spark community impact its future?
    a) By improving the core engine
    b) By enhancing security features
    c) By adding support for new data formats
    d) All of the above

Answers