MCQs on Introduction to Hadoop and HDFS | Hadoop HDFS
Explore Hadoop and HDFS with 30 multiple-choice questions covering Big Data concepts, Hadoop ecosystem, HDFS architecture, key components like blocks, Data Nodes, Name Nodes, and differences between HDFS and traditional file systems.
1. Overview of Big Data and Hadoop Ecosystem
- What is the primary characteristic of Big Data?
- A) Data that is structured and small in size
- B) Data that cannot be stored in a single machine
- C) Data that is only textual
- D) Data that is processed on a centralized server
- What is Hadoop designed to handle?
- A) Small-scale data processing
- B) Large-scale, distributed data storage and processing
- C) Relational database management
- D) Cloud storage only
- What is the Hadoop Ecosystem?
- A) A collection of libraries for managing databases
- B) A set of tools that allow the processing and storage of large datasets
- C) A platform for building traditional applications
- D) A cloud platform for storing files
- Which of the following is a core component of Hadoop?
- A) HDFS
- B) MySQL
- C) MongoDB
- D) Apache Spark
- What does Hadoop primarily focus on?
- A) Managing relational data
- B) Processing structured data only
- C) Storing and processing massive volumes of data in parallel
- D) Storing only unstructured data
2. What is Hadoop HDFS?
- What does HDFS stand for?
- A) High Definition File Storage
- B) Hadoop Distributed File System
- C) Hadoop Data File System
- D) Hyper Data File System
- What is the primary function of HDFS?
- A) Data encryption
- B) Storing files across multiple machines in a distributed manner
- C) Data analytics
- D) Running complex SQL queries
- How is data stored in HDFS?
- A) In a single file on one machine
- B) Across multiple Data Nodes in blocks
- C) In a single table format
- D) In relational databases
- What does the HDFS block size refer to?
- A) The size of individual files
- B) The size of data chunks in which files are stored across Data Nodes
- C) The number of Name Nodes in a cluster
- D) The size of each directory
- What is the default block size in HDFS?
- A) 32 MB
- B) 64 MB
- C) 128 MB
- D) 256 MB
3. Key Concepts: Blocks, Data Nodes, Name Nodes
- What is the role of the Data Node in HDFS?
- A) To store and manage metadata
- B) To execute map-reduce jobs
- C) To store actual data blocks
- D) To manage distributed processing tasks
- What is the Name Node in HDFS responsible for?
- A) Storing data blocks
- B) Managing the file system namespace and metadata
- C) Handling job execution
- D) Storing logs of file accesses
- How are files split in HDFS?
- A) Into single units called partitions
- B) Into segments based on the file’s size
- C) Into data blocks of fixed size
- D) Into rows and columns
- What is the purpose of block replication in HDFS?
- A) To store each file on a different machine
- B) To ensure fault tolerance by creating multiple copies of data blocks
- C) To reduce the number of machines required for storing data
- D) To reduce network bandwidth usage
- How many replicas of data blocks are created by default in HDFS?
- A) 1
- B) 2
- C) 3
- D) 4
4. Hadoop Architecture and Components
- Which of the following is NOT a component of Hadoop?
- A) HDFS
- B) MapReduce
- C) HBase
- D) MySQL
- What is the primary function of the Hadoop Job Tracker?
- A) To store job results
- B) To schedule and monitor MapReduce jobs
- C) To manage job failures
- D) To execute MapReduce tasks
- In the Hadoop architecture, which component manages the storage layer?
- A) Job Tracker
- B) Resource Manager
- C) Name Node
- D) Data Node
- What is the function of the Resource Manager in Hadoop?
- A) To manage job scheduling
- B) To manage cluster resources and allocate tasks
- C) To execute MapReduce jobs
- D) To store metadata
- Which of the following is used for managing and storing large amounts of data in Hadoop?
- A) HDFS
- B) HBase
- C) MapReduce
- D) Hive
5. HDFS vs Traditional File Systems
- How does HDFS differ from traditional file systems?
- A) HDFS stores data in a single machine, while traditional systems distribute data
- B) HDFS is designed for parallel processing, whereas traditional file systems are not
- C) Traditional file systems use data replication, while HDFS does not
- D) HDFS is a proprietary system, while traditional file systems are open-source
- What is the main advantage of HDFS over traditional file systems?
- A) Better compatibility with relational databases
- B) Optimized for handling very large files with high throughput
- C) It can only store unstructured data
- D) It provides real-time data access
- How is fault tolerance handled in traditional file systems versus HDFS?
- A) Traditional file systems use mirroring, while HDFS uses block replication
- B) HDFS does not support fault tolerance, unlike traditional systems
- C) Both systems use the same fault tolerance techniques
- D) Traditional file systems do not handle fault tolerance
- In traditional file systems, what happens when a file exceeds the disk size?
- A) The file is split into multiple disks
- B) The file cannot be saved
- C) It is compressed
- D) It creates multiple replicas automatically
- Which of the following is a disadvantage of traditional file systems compared to HDFS?
- A) Inability to store large files
- B) Lack of scalability
- C) High cost for scaling
- D) Difficulty in managing large data volumes
- Which of the following is true about HDFS compared to traditional file systems?
- A) HDFS is designed for high-throughput access to large datasets, while traditional file systems are for low-latency access
- B) Traditional file systems are optimized for massive data processing
- C) HDFS supports low-latency access to small files, unlike traditional systems
- D) Traditional file systems store data in fixed-size blocks, while HDFS uses dynamic-sized blocks
- How does HDFS ensure data availability in case of node failure?
- A) By storing each block in multiple copies across different nodes
- B) By storing each file in a single node
- C) By using RAID storage
- D) By automatically transferring data to cloud storage
- Which of these is NOT a feature of HDFS?
- A) It stores data in fixed-size blocks
- B) It is designed for sequential read/write access to large files
- C) It uses a single Name Node
- D) It stores data in relational databases
- What is the maximum file size supported by HDFS?
- A) 1 GB
- B) 10 GB
- C) Unlimited (theoretically)
- D) 1 TB
- How does HDFS improve performance when reading data?
- A) By caching data in memory
- B) By sequentially reading data from a single machine
- C) By distributing the data to multiple nodes and reading in parallel
- D) By compressing data before reading
Answers Table:
| Qno | Answer |
|---|---|
| 1 | B) Data that cannot be stored in a single machine |
| 2 | B) Large-scale, distributed data storage and processing |
| 3 | B) A set of tools that allow the processing and storage of large datasets |
| 4 | A) HDFS |
| 5 | C) Storing and processing massive volumes of data in parallel |
| 6 | B) Hadoop Distributed File System |
| 7 | B) Storing files across multiple machines in a distributed manner |
| 8 | B) Across multiple Data Nodes in blocks |
| 9 | B) The size of data chunks in which files are stored across Data Nodes |
| 10 | C) 128 MB |
| 11 | C) To store actual data blocks |
| 12 | B) Managing the file system namespace and metadata |
| 13 | C) Into data blocks of fixed size |
| 14 | B) To ensure fault tolerance by creating multiple copies of data blocks |
| 15 | C) 3 |
| 16 | D) MySQL |
| 17 | B) To schedule and monitor MapReduce jobs |
| 18 | C) Name Node |
| 19 | B) To manage cluster resources and allocate tasks |
| 20 | A) HDFS |
| 21 | B) HDFS is designed for parallel processing, whereas traditional file systems are not |
| 22 | B) Optimized for handling very large files with high throughput |
| 23 | A) Traditional file systems use mirroring, while HDFS uses block replication |
| 24 | A) The file is split into multiple disks |
| 25 | B) Lack of scalability |
| 26 | A) HDFS is designed for high-throughput access to large datasets, while traditional file systems are for low-latency access |
| 27 | A) By storing each block in multiple copies across different nodes |
| 28 | D) It stores data in relational databases |
| 29 | C) Unlimited (theoretically) |
| 30 | C) By distributing the data to multiple nodes and reading in parallel |