Role of Hadoop and MapReduce MCQs December 19, 2025November 19, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the primary function of Hadoop in big data processing? (A) To store and process large volumes of unstructured data across distributed systems (B) To store and process large volumes of structured data only (C) To process small datasets on a single server (D) To clean and preprocess data only 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 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 used for indexing and searching data (C) HDFS is a storage system designed to store large datasets across a cluster of machines (D) HDFS is a data mining algorithm 4. In MapReduce, what is the role of the Mapper? (A) It maps input data into key-value pairs for processing (B) It reduces the data to a smaller output (C) It stores the final output data into the Hadoop Distributed File System (HDFS) (D) It performs error checking and debugging 5. In MapReduce, what is the role of the Reducer? (A) It reads and splits the data into smaller pieces (B) It takes the output from the Mapper and aggregates or processes it into a final result (C) It stores processed data in a local storage system (D) It performs data cleaning and transformation 6. How does MapReduce handle data processing in a distributed environment? (A) By splitting the input data into chunks and processing each chunk on different machines (B) By processing all data on a single machine (C) By creating backups of data on each node (D) By using a centralized system to process data 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 reduces the need for storage systems (D) It allows for parallel processing of large datasets across distributed nodes 8. What is the main advantage of using HDFS in the Hadoop ecosystem? (A) It enables real-time processing of data (B) It is a relational database management system (C) It performs machine learning algorithms on data (D) It can store large volumes of data reliably across multiple machines 9. What happens during the Shuffle and Sort phase in MapReduce? (A) The output of the Mapper is shuffled and sorted based on the key-value pairs (B) Data is divided into smaller chunks for parallel processing (C) Data is stored in the Hadoop Distributed File System (HDFS) (D) The final output is aggregated and saved 10. Which of the following is NOT a component of the Hadoop ecosystem? (A) Hadoop Distributed File System (HDFS) (B) MapReduce (C) MongoDB (D) Apache Spark 11. What is the main disadvantage of using MapReduce for real-time processing? (A) It is not well-suited for low-latency, real-time processing (B) It does not support distributed data processing (C) It requires manual intervention for data storage (D) It only processes small amounts of data 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 13. What is the role of the Job Tracker in MapReduce? (A) It stores the processed data in HDFS (B) It schedules and monitors the tasks in the MapReduce job (C) It splits the input data into smaller chunks for the Mapper (D) It performs data cleaning before processing 14. What is the main benefit of Hadoop’s fault tolerance? (A) It guarantees zero errors in the processing of data (B) It makes data processing faster (C) It automatically recovers from hardware failures by replicating data across nodes (D) It reduces the need for parallel processing 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