MCQs on Spark SQL and DataFrames | Apache Spark MCQs Questions

This collection of Apache Spark MCQs Questions focuses on Spark SQL and DataFrames, critical for working with structured data in big data processing. Topics include SQL query execution in Spark, optimizing DataFrame operations, schema manipulation, Catalyst Optimizer, and integration with Hive and JDBC. These questions will help you strengthen your knowledge for interviews and certification exams, ensuring you understand key concepts in Spark SQL and DataFrames.

MCQs

1. SQL Query Execution in Spark

1. Which component in Spark is responsible for executing SQL queries?
   a) Spark Core
   b) Spark SQL
   c) Spark Streaming
   d) Spark MLlib
2. What is a primary feature of Spark SQL?
   a) Real-time processing
   b) Batch processing of structured data
   c) Distributed machine learning
   d) Graph processing
3. Which command is used to register a DataFrame as a temporary view in Spark?
   a) createOrReplaceView
   b) createOrReplaceTempView
   c) registerView
   d) registerTempView
4. Spark SQL supports which of the following languages for queries?
   a) Python and R
   b) SQL and HiveQL
   c) Scala and Java
   d) None of the above
5. How does Spark SQL optimize query execution?
   a) By caching intermediate results
   b) By compiling queries to machine code
   c) By using Catalyst Optimizer
   d) By reducing memory usage
6. Which storage formats are supported for SQL query execution in Spark?
   a) CSV, JSON, and Parquet
   b) Only CSV
   c) MySQL database
   d) None of the above

2. DataFrame Operations and Optimization

7. What is the primary abstraction for working with structured data in Spark?
   a) RDD
   b) DataFrame
   c) Dataset
   d) Table
8. Which operation is used to filter rows in a DataFrame?
   a) select
   b) filter
   c) groupBy
   d) orderBy
9. Spark DataFrame optimization is achieved through:
   a) Dynamic memory allocation
   b) Lazy evaluation and Catalyst Optimizer
   c) Thread pooling
   d) MapReduce engine
10. What is the primary method to join two DataFrames in Spark?
    a) combine
    b) merge
    c) join
    d) union
11. Which method is used to apply transformations on DataFrames?
    a) transform
    b) apply
    c) map
    d) flatMap
12. What is the recommended file format for optimized DataFrame storage?
    a) JSON
    b) CSV
    c) Parquet
    d) Text

3. Schema Inference and Manipulation

13. In Spark, schema inference occurs during:
    a) DataFrame creation
    b) RDD creation
    c) Query execution
    d) Job submission
14. Which method allows you to define a schema explicitly in Spark?
    a) StructType and StructField
    b) SchemaType and SchemaField
    c) DataType and FieldType
    d) None of the above
15. What is a schema in Spark?
    a) The data partition strategy
    b) A description of data columns and their types
    c) A storage format
    d) A job execution plan
16. How do you retrieve the schema of a DataFrame in Spark?
    a) df.getSchema()
    b) df.schema()
    c) df.printSchema()
    d) df.showSchema()
17. Which operation allows adding a new column to a DataFrame?
    a) add
    b) withColumn
    c) append
    d) newColumn
18. How can you cast a column to a different data type in Spark?
    a) cast
    b) transform
    c) changeType
    d) convertType

4. Catalyst Optimizer and Query Execution

19. What is the Catalyst Optimizer in Spark?
    a) A tool for managing cluster resources
    b) A query optimization engine
    c) A scheduling algorithm
    d) A DataFrame caching mechanism
20. Catalyst Optimizer improves query performance by:
    a) Generating multiple execution plans and selecting the best one
    b) Reducing network latency
    c) Increasing memory capacity
    d) Adding more nodes to the cluster
21. In Spark SQL, physical query plans are generated after:
    a) Logical optimization
    b) Schema inference
    c) Data transformation
    d) Job execution
22. Which optimization technique does Catalyst NOT use?
    a) Predicate pushdown
    b) Column pruning
    c) Join reordering
    d) Memory swapping
23. What is the default execution engine for Spark SQL queries?
    a) HiveQL
    b) MapReduce
    c) Tungsten execution engine
    d) Flink engine
24. How does Catalyst Optimizer handle user-defined functions (UDFs)?
    a) By compiling them to JVM bytecode
    b) By optimizing them internally
    c) By treating them as black boxes
    d) By converting them to SQL queries

5. Integration with Hive and JDBC

25. What does Spark use to integrate with Hive?
    a) Hive JDBC Driver
    b) Hive Metastore
    c) Hive Query Language (HQL)
    d) Hive CLI
26. How does Spark SQL connect to external databases?
    a) Using Hive tables
    b) Using JDBC connectors
    c) Through the Catalyst Optimizer
    d) By defining external schemas
27. Which Hive functionality is NOT supported by Spark SQL?
    a) Complex data types
    b) ACID transactions
    c) Partitioned tables
    d) User-defined functions
28. To configure Hive integration in Spark, which property must be set?
    a) spark.sql.hive.enable
    b) spark.sql.hive.support.enabled
    c) hive.metastore.enabled
    d) spark.sql.catalogImplementation=hive
29. How can Spark DataFrames be written to a database?
    a) Using the write.jdbc() method
    b) Using the saveAsTable() method
    c) Using the toDatabase() method
    d) Using the jdbc.save() method
30. What is a benefit of using JDBC for Spark integration with external systems?
    a) Low-latency data transfer
    b) Support for distributed queries
    c) Direct connection to relational databases
    d) Improved fault tolerance

Answers