1. What is the primary function of Hadoop in big data processing?
A. To store and process large volumes of structured data only
B. To store and process large volumes of unstructured data across distributed systems
C. To process small datasets on a single server
D. To clean and preprocess data only
Answer: B
(Hadoop is a framework that allows the storage and processing of large volumes of unstructured and structured data across distributed systems.)
2. What does MapReduce primarily do in the Hadoop ecosystem?
A. It stores data in distributed systems
B. It maps data into a format suitable for storage
C. It processes and analyzes large datasets in parallel across multiple nodes
D. It encrypts data for security during transmission
Answer: C
(MapReduce is a processing model in Hadoop that splits the data into smaller chunks and processes them in parallel across multiple nodes.)
3. Which of the following is true about Hadoop Distributed File System (HDFS)?
A. HDFS is used to perform real-time data analytics
B. HDFS is a storage system designed to store large datasets across a cluster of machines
C. HDFS is used for indexing and searching data
D. HDFS is a data mining algorithm
Answer: B
(HDFS is a distributed storage system designed to store large datasets across multiple machines in a Hadoop cluster.)
4. In MapReduce, what is the role of the Mapper?
A. It reduces the data to a smaller output
B. It maps input data into key-value pairs for processing
C. It stores the final output data into the Hadoop Distributed File System (HDFS)
D. It performs error checking and debugging
Answer: B
(The Mapper in MapReduce processes input data and maps it into key-value pairs, which are then passed to the Reducer for further processing.)
5. In MapReduce, what is the role of the Reducer?
A. It reads and splits the data into smaller pieces
B. It stores processed data in a local storage system
C. It takes the output from the Mapper and aggregates or processes it into a final result
D. It performs data cleaning and transformation
Answer: C
(The Reducer takes the output from the Mapper, aggregates or processes it, and produces the final output.)
6. How does MapReduce handle data processing in a distributed environment?
A. By processing all data on a single machine
B. By splitting the input data into chunks and processing each chunk on different machines
C. By creating backups of data on each node
D. By using a centralized system to process data
Answer: B
(MapReduce splits the input data into smaller chunks and processes each chunk in parallel on different machines in the distributed environment.)
7. What is the advantage of using Hadoop MapReduce for large-scale data processing?
A. It provides centralized data processing
B. It offers real-time data processing capabilities
C. It allows for parallel processing of large datasets across distributed nodes
D. It reduces the need for storage systems
Answer: C
(Hadoop MapReduce enables parallel processing of large datasets across multiple distributed nodes, making it highly efficient for big data analysis.)
8. What is the main advantage of using HDFS in the Hadoop ecosystem?
A. It enables real-time processing of data
B. It can store large volumes of data reliably across multiple machines
C. It performs machine learning algorithms on data
D. It is a relational database management system
Answer: B
(HDFS is designed to store large volumes of data reliably across multiple machines in a distributed system, ensuring fault tolerance and scalability.)
9. What happens during the Shuffle and Sort phase in MapReduce?
A. Data is divided into smaller chunks for parallel processing
B. The output of the Mapper is shuffled and sorted based on the key-value pairs
C. Data is stored in the Hadoop Distributed File System (HDFS)
D. The final output is aggregated and saved
Answer: B
(During the Shuffle and Sort phase, the output from the Mapper is shuffled and sorted based on the key-value pairs before it is passed to the Reducer.)
10. Which of the following is NOT a component of the Hadoop ecosystem?
A. Hadoop Distributed File System (HDFS)
B. MapReduce
C. Apache Spark
D. MongoDB
Answer: D
(MongoDB is not part of the Hadoop ecosystem; it is a NoSQL database, while HDFS and MapReduce are integral components of Hadoop.)
11. What is the main disadvantage of using MapReduce for real-time processing?
A. It requires manual intervention for data storage
B. It does not support distributed data processing
C. It is not well-suited for low-latency, real-time processing
D. It only processes small amounts of data
Answer: C
(MapReduce is batch-oriented and is not well-suited for real-time, low-latency processing, making it less effective for tasks that require immediate results.)
12. Which of the following is true about Hadoop’s scalability?
A. It can only process small datasets
B. It is not suitable for horizontal scaling
C. It can scale horizontally to handle petabytes of data by adding more nodes
D. It requires special hardware for scaling
Answer: C
(Hadoop is designed to scale horizontally by adding more nodes to the cluster, allowing it to handle massive datasets across a distributed environment.)
**13. What is the role of the Job Tracker in MapReduce?
A. It stores the processed data in HDFS
B. It splits the input data into smaller chunks for the Mapper
C. It schedules and monitors the tasks in the MapReduce job
D. It performs data cleaning before processing
Answer: C
(The Job Tracker is responsible for scheduling and monitoring the tasks in the MapReduce job, managing the distribution of tasks across the cluster.)
14. What is the main benefit of Hadoop’s fault tolerance?
A. It guarantees zero errors in the processing of data
B. It automatically recovers from hardware failures by replicating data across nodes
C. It makes data processing faster
D. It reduces the need for parallel processing
Answer: B
(Hadoop ensures fault tolerance by replicating data across multiple nodes so that in case of node failure, the data can still be accessed from other nodes.)
15. What is Hadoop YARN responsible for in the Hadoop ecosystem?
A. Managing and allocating resources for distributed processing
B. Storing data across the cluster
C. Sorting data for MapReduce jobs
D. Handling real-time data streams
Answer: A
(Hadoop YARN (Yet Another Resource Negotiator) is responsible for managing and allocating resources for the various applications running in the Hadoop ecosystem.)