Hadoop Online Exam - US Data Technologioes

NameNodes in a Hadoop cluster are responsible for storing the metadata of the files and directories in the cluster. They keep track of the location of data blocks and manage the overall file system namespace. Since they handle such important tasks, NameNodes are typically high storage machines in the cluster to accommodate the large amount of metadata. Therefore, the statement that NameNodes are usually high storage machines in the clusters is true.

Hadoop is an open-source framework for processing and storing large data sets. Being open source means that the source code of Hadoop is freely available to the public, allowing anyone to view, modify, and distribute it. Therefore, the statement "Hadoop is open source" is always true, regardless of any specific implementation or vendor.

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Hive is used as a Hadoop query engine. It provides a SQL-like interface to query and analyze data stored in Hadoop. It translates SQL queries into MapReduce jobs, allowing users to leverage the power of Hadoop for data processing and analysis. Hive also provides a schema on read feature, which allows users to apply structure to data stored in Hadoop, making it easier to query and analyze. Therefore, the correct answer is "Hadoop query engine".

Commodity hardware in the Hadoop world refers to very cheap hardware. This means that the hardware used in a Hadoop cluster is inexpensive and readily available, as opposed to high-end or specialized hardware. The use of commodity hardware allows for cost-effective scalability and fault tolerance in Hadoop systems, as individual hardware components can be easily replaced or upgraded without significant financial investment.

The correct HDFS command to create a copy of a file from a local system is "copyFromLocal". This command is used to copy a file or directory from the local file system to the HDFS file system.

The given correct answer is "Parsing 5 MB XML file every 5 minutes." This answer suggests that the person prefers the task of parsing a 5 MB XML file every 5 minutes over the other options.

The correct answer is "cut". The "cut" command in HDFS is used to create a cut of a file within HDFS. This command allows users to select specific fields or sections of a file and extract them into a new file. It is commonly used for data manipulation and analysis purposes, as it allows users to easily extract and work with specific portions of a file without modifying the original file.

Hadoop Pseudo Distributed Mode runs on multiple machines.

HBase is used as a tool for random and fast read/write operations in Hadoop. It provides a distributed, scalable, and consistent database for storing and retrieving large amounts of structured and semi-structured data. HBase is designed to handle high volumes of data with low-latency access, making it suitable for applications that require real-time access to data. It is often used for use cases such as real-time analytics, log processing, and recommendation systems.

Hadoop is not a tool for OLTP (Online Transaction Processing). It is a tool for handling big data and performing batch processing on large datasets. It is designed for processing and analyzing structured and unstructured data, and it aims for horizontal scaling out/in scenarios, not vertical scaling. Therefore, the correct answer is "It's a tool for OLTP."

The default HDFS block size is 32 MB. This means that when a file is stored in HDFS, it will be divided into blocks of 32 MB each. This block size is configurable and can be changed based on the requirements of the system. A larger block size can improve performance by reducing the overhead of managing smaller blocks, but it can also result in wasted space if the file is smaller than the block size. Conversely, a smaller block size can reduce wasted space but may increase the overhead of managing a larger number of blocks.

Hive supports custom extensions written in the C programming language.

FileInputFormat is the correct answer because it is a class in Hadoop that is responsible for converting inputs, such as files, into key-value pairs for the MapReduce process. It is used as the input format for MapReduce jobs and handles the splitting of input files into input splits, which are then processed by the RecordReader. The RecordReader is responsible for reading the records within each input split and converting them into key-value pairs. Therefore, FileInputFormat plays a crucial role in the input phase of the MapReduce process.