Big Data Computing
1. Identify this aircraft:
Solution:
Correct answer: A) HUL-26 Pushpak
2.Which of the following statement(s) is/are TRUE about the “Troposphere” in ISA?
Solution:
Correct answer: A) It is the bottom most layer in Earth’s atmosphere.
Explanation:
MapReduce is a programming model used for processing and generating large data sets. It involves two main steps: mapping and reducing. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs. The map function operates in parallel across the input data. The intermediate key/value pairs are then grouped by key and passed to a reduce function, which merges all intermediate values associated with the same intermediate key. This process allows for distributed and parallel processing of large datasets.
Option A is incorrect because MapReduce does involve intermediate steps (mapping and reducing) to process data.
Option B is incorrect because while MapReduce is used for processing unstructured data, its primary purpose is not to convert it into structured data.
Option D is incorrect because MapReduce is not primarily focused on creating visualizations and graphs; its main focus is on processing and generating large data sets using the map and reduce functions.
3. _________________ is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
Solution:
Correct answer: A) Flume
Explanation:
Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and very flexible architecture based on streaming data flows. It's quite robust and fault-tolerant, and it's highly tunable to enhance reliability mechanisms, failover, recovery, and other mechanisms that keep the cluster safe and reliable. Flume is used for ingesting log and event data into data stores or processing frameworks for analysis.
4. What is the primary purpose of YARN (Yet Another Resource Manager) in the Apache Hadoop ecosystem?
Solution:
Correct answer: C) YARN is responsible for allocating system resources and scheduling tasks for applications in a Hadoop cluster.
Explanation:
YARN, which stands for "Yet Another Resource Manager," is a key component of the Apache Hadoop ecosystem. Its primary role is resource management and job scheduling. YARN is responsible for efficiently allocating system resources, such as CPU and memory, to various applications running in a Hadoop cluster. It also handles the scheduling of tasks to be executed on different cluster nodes, ensuring optimal utilization of resources and improving overall cluster performance.
5. Which of the following statements accurately describes the characteristics and functionality of HDFS (Hadoop Distributed File System)?
Solution:
Correct answer: C) HDFS is a distributed, scalable, and portable file system designed for storing large files across multiple machines, achieving reliability through replication.
Explanation:
HDFS (Hadoop Distributed File System) is a fundamental component of the Hadoop framework. It is designed to store and manage large files across a distributed cluster of machines. The key features and functionality of HDFS include:
- Distributed and Scalable: HDFS distributes data across multiple nodes in a cluster, allowing it to handle large datasets that range from gigabytes to terabytes, and even petabytes. It scales horizontally as more nodes are added to the cluster.
- Reliability Through Replication: HDFS achieves reliability by replicating data blocks across multiple data nodes in the cluster. This replication ensures data availability even in the face of node failures.
- Single Name Node and Data Nodes: Each Hadoop instance typically includes a single name node, which acts as the metadata manager for the file system, and a cluster of data nodes that store the actual data.
- Portability: HDFS is written in Java and is designed to be portable across different platforms and operating systems.
Option A is incorrect because HDFS is not centralized; it is distributed and designed for storing large files.
Option B is incorrect because HDFS is not a programming language; it's a file system.
Option D is incorrect because HDFS is not a visualization tool; it is a distributed file system for storing and managing data in the Hadoop ecosystem.
6. Which statement accurately describes the role and design of HBase in the Hadoop stack?
Solution:
Correct answer: C) HBase is a key-value store that provides fast random access to substantial datasets, making it suitable for applications requiring such access patterns.
Explanation:
HBase is a NoSQL database that is a key component of the Hadoop ecosystem. Its design focuses on providing high-speed random access to large amounts of data. Key characteristics and roles of HBase include:
- Key-Value Store: HBase stores data in a distributed, column-family-oriented fashion, similar to a key-value store. It allows you to look up data quickly using a key.
- Fast Random Access: HBase is optimized for fast read and write operations, particularly random access patterns. This makes it suitable for applications that require quick retrieval of specific data points from massive datasets.
- Scalability: HBase is designed to scale horizontally, allowing it to handle vast amounts of data by adding more nodes to the cluster.
Option A is incorrect because HBase is not a programming language; it's a database system.
Option B is incorrect because HBase is not a data warehousing solution; it's designed for real-time, random access to data rather than batch processing.
Option D is incorrect because HBase is not a visualization tool; it's a database system focused on high-speed data access.
7. _____________ brings scalable parallel database technology to Hadoop and allows users to submit low latencies queries to the data that's stored within the HDFS or the Hbase without acquiring a ton of data movement and manipulation.
Solution:
Correct answer: A) Impala
Explanation:
Impala is a query engine that brings scalable parallel database technology to Hadoop. It allows users to submit low-latency queries to the data stored within the HDFS or the Hbase without requiring extensive data movement and manipulation. Impala is designed for interactive and real-time querying of data in the Hadoop ecosystem, making it suitable for business intelligence and data analytics tasks.
8. What is the primary purpose of ZooKeeper in a distributed system?
Solution:
Correct answer: C) ZooKeeper is a highly reliable distributed coordination kernel used for tasks such as distributed locking, configuration management, leadership election, and work queues.
Explanation:
ZooKeeper is a distributed coordination service that provides a reliable and efficient way for coordinating various processes and components in a distributed system. It offers functionalities like distributed locking, configuration management, leader election, and work queues to ensure that distributed applications can work together effectively. ZooKeeper acts as a central repository for managing metadata related to the coordination of these distributed tasks.
9. ________________ is a distributed file system that stores data on a commodity machine. Providing very high aggregate bandwidth across the entire cluster.
Solution:
Correct answer: B) Hadoop Distributed File System (HDFS)
Explanation:
Hadoop Distributed File System (HDFS) is a distributed file system that stores data on commodity machines. It provides very high aggregate bandwidth across the entire cluster, allowing for the efficient storage and retrieval of data in Hadoop clusters. HDFS is a fundamental component of the Hadoop ecosystem and is designed to handle large volumes of data across distributed nodes.
10. Which statement accurately describes Spark MLlib?
Solution:
Correct answer: C) Spark MLlib is a distributed machine learning framework built on top of Spark Core, providing scalable machine learning algorithms and utilities for tasks such as classification, regression, clustering, and collaborative filtering.
Explanation:
Spark MLlib (Machine Learning Library) is a component of the Apache Spark ecosystem. It offers a distributed machine learning framework that allows developers to leverage Spark's distributed computing capabilities for scalable and efficient machine learning tasks. Key features and roles of Spark MLlib include:
- Distributed Machine Learning: MLlib provides a wide range of machine learning algorithms that are designed to work efficiently in a distributed environment. It enables the processing of large datasets across a cluster of machines.
- Common Learning Algorithms: MLlib includes a variety of common machine learning algorithms, such as classification, regression, clustering, and collaborative filtering.
- Integration with Spark Core: MLlib is built on top of Spark Core, which provides the underlying distributed processing framework. This integration allows seamless utilization of Spark's data processing capabilities for machine learning tasks.
Option A is incorrect because Spark MLlib is not a visualization tool; its focus is on distributed machine learning.
Option B is incorrect because Spark MLlib is not a programming language; it's a machine learning library.
Option D is incorrect because Spark MLlib is not a data warehousing solution; its primary purpose is machine learning on distributed data.
Comments
Post a Comment