MCQs on Introduction to Apache Spark | Apache Spark MCQs Questions
Apache Spark is a powerful, open-source framework designed for big data processing and analytics. Its advanced capabilities enable distributed computing and rapid data processing, making it a preferred choice in data-driven industries. These Apache Spark MCQs questions cover key topics, including the Spark ecosystem, architecture, cluster modes, and data structures such as RDDs, DataFrames, and Datasets, providing valuable insights for beginners and professionals.
MCQs: Overview of Big Data Processing
- What is the main purpose of big data processing frameworks like Apache Spark?
a) Data storage
b) Distributed data processing
c) Visualization
d) Network security - Which characteristic defines big data?
a) High complexity
b) Small volume
c) No structure
d) Fast processing - Apache Spark is best suited for:
a) Real-time data analytics
b) Database administration
c) Image editing
d) Video streaming - Big data processing frameworks are needed to handle data with:
a) Small volume
b) High velocity, volume, and variety
c) Simple patterns
d) No structure - Which of the following is NOT a component of big data processing?
a) Data ingestion
b) Machine learning
c) Mobile app development
d) Data storage
MCQs: Evolution and Need for Apache Spark
- Apache Spark was originally developed at:
a) MIT
b) UC Berkeley
c) Stanford University
d) Harvard University - Spark was created to address the limitations of:
a) Hadoop MapReduce
b) SQL databases
c) NoSQL databases
d) Data lakes - What makes Apache Spark faster than Hadoop MapReduce?
a) Dependency on NoSQL databases
b) In-memory processing
c) Use of Java language
d) Lack of fault tolerance - Apache Spark is particularly known for its:
a) Low memory usage
b) Batch and real-time processing capabilities
c) Lack of scalability
d) Limited API support - Which version of Apache Spark introduced structured streaming?
a) Spark 1.0
b) Spark 2.0
c) Spark 3.0
d) Spark 2.5
MCQs: Apache Spark Ecosystem Components
- Which of the following is a core component of the Apache Spark ecosystem?
a) Hive
b) Spark Streaming
c) HDFS
d) Cassandra - Spark SQL is used for:
a) Real-time data ingestion
b) Querying structured data
c) Machine learning models
d) Data visualization - What does MLlib in Spark provide?
a) Data storage
b) Machine learning capabilities
c) Networking tools
d) Security protocols - Which library in Spark handles graph processing?
a) Spark SQL
b) GraphX
c) MLlib
d) Spark Core - The component of Apache Spark that supports real-time processing is:
a) Spark SQL
b) Spark Streaming
c) GraphX
d) HDFS
MCQs: Spark Architecture and Cluster Modes
- What is the central component of the Spark architecture?
a) Driver program
b) Executor
c) Master node
d) Data source - Which mode allows Spark to run locally on a single machine?
a) Client mode
b) Local mode
c) Cluster mode
d) Executor mode - How does Spark achieve fault tolerance?
a) Data replication
b) Use of secondary servers
c) Resilient Distributed Datasets (RDDs)
d) Automatic backups - What is the role of the Spark driver?
a) Store data permanently
b) Define transformations and actions
c) Monitor Spark applications
d) Load external libraries - In a cluster mode setup, what manages the cluster resources?
a) Executors
b) SparkContext
c) Cluster manager
d) Driver program
MCQs: Introduction to RDDs, DataFrames, and Datasets
- What does RDD stand for in Apache Spark?
a) Relational Data Distribution
b) Resilient Distributed Dataset
c) Rapid Data Distribution
d) Random Data Distribution - Which of the following is an immutable collection in Spark?
a) DataFrames
b) RDDs
c) Datasets
d) Tables - DataFrames in Spark are:
a) Optimized for machine learning
b) Similar to SQL tables
c) Designed for unstructured data
d) Used only in Hadoop - What is a Dataset in Apache Spark?
a) An advanced abstraction for Java and Scala
b) A data visualization tool
c) A component of Spark Streaming
d) A storage layer - Which API provides better optimization for queries?
a) RDD
b) DataFrame
c) Dataset
d) Hadoop FS
General Knowledge MCQs on Apache Spark
- Apache Spark is written in which programming language?
a) Java
b) Scala
c) Python
d) R - Which deployment mode is suitable for distributed environments?
a) Local mode
b) Standalone mode
c) Cluster mode
d) Client mode - How does Apache Spark process data?
a) In-memory
b) On-disk only
c) In-database
d) Using NoSQL - Which scheduler does Apache Spark use by default?
a) Fair Scheduler
b) FIFO Scheduler
c) Round-robin Scheduler
d) Hadoop Scheduler - Apache Spark integrates seamlessly with:
a) Hadoop and YARN
b) MySQL
c) Oracle DB
d) Cassandra only
Answers Table
| Qno | Answer (Option with Text) |
|---|---|
| 1 | b) Distributed data processing |
| 2 | a) High complexity |
| 3 | a) Real-time data analytics |
| 4 | b) High velocity, volume, and variety |
| 5 | c) Mobile app development |
| 6 | b) UC Berkeley |
| 7 | a) Hadoop MapReduce |
| 8 | b) In-memory processing |
| 9 | b) Batch and real-time processing capabilities |
| 10 | b) Spark 2.0 |
| 11 | b) Spark Streaming |
| 12 | b) Querying structured data |
| 13 | b) Machine learning capabilities |
| 14 | b) GraphX |
| 15 | b) Spark Streaming |
| 16 | a) Driver program |
| 17 | b) Local mode |
| 18 | c) Resilient Distributed Datasets (RDDs) |
| 19 | b) Define transformations and actions |
| 20 | c) Cluster manager |
| 21 | b) Resilient Distributed Dataset |
| 22 | b) RDDs |
| 23 | b) Similar to SQL tables |
| 24 | a) An advanced abstraction for Java and Scala |
| 25 | b) DataFrame |
| 26 | b) Scala |
| 27 | c) Cluster mode |
| 28 | a) In-memory |
| 29 | b) FIFO Scheduler |
| 30 | a) Hadoop and YARN |