Q111. In the context of Big Data, the term "Volume" refers to:
Computers, Information and Communication Technology: RAS Prelims MCQs
200 RAS Prelims MCQs on computers, information and communication technology cover the generations of computers, the parts of the CPU, memory types and networking. The first and second generations, analog computers, the control unit, cache and RAM, the memory hierarchy and the SSD are asked as definitions and sequences, with the explanations giving the reason for each design.
Practice questions based on the RPSC RAS Prelims syllabus. They follow the exam pattern but are not past-paper questions.
Showing 111–120 of 200 questions
Q112. Consider the 5Vs of Big Data:
I. Volume describes data magnitude.
II. Velocity refers to the speed of data processing and generation.
III. Variety indicates the different types of structured and unstructured data.
IV. Veracity reflects the quality, accuracy, and reliability of the data.
V. Value represents the actionable insights gained from the data.
Which of the above statement(s) is/are correct?
I. Volume describes data magnitude.
II. Velocity refers to the speed of data processing and generation.
III. Variety indicates the different types of structured and unstructured data.
IV. Veracity reflects the quality, accuracy, and reliability of the data.
V. Value represents the actionable insights gained from the data.
Which of the above statement(s) is/are correct?
Explanation
Big Data is defined by the 5Vs: Volume, Velocity, Variety, Veracity, and Value. These characteristics describe the scale, speed, diversity, reliability, and ultimately the usefulness of the information. Together, they provide a comprehensive framework for understanding the challenges and opportunities of analyzing massive datasets. Mastering these five dimensions is essential for organizations to derive meaningful and actionable insights from.
Q113. Identify the incorrect statement regarding data types in Big Data.
Explanation
Big Data encompasses a wide range of formats, including structured data in relational databases, semi-structured data like JSON, and unstructured data such as videos and social media posts. The strength of Big Data analytics lies in its ability to process all these diverse types, not just traditional tabular data. By integrating unstructured information, organizations can gain a more complete.
Q114. A hospital system analyzes millions of patient records to predict potential disease outbreaks before they occur. This is an application of:
Explanation
Predictive analytics involves using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. In healthcare, analyzing patient records to forecast disease outbreaks is a powerful application of this technology. By identifying patterns and trends early, medical professionals can take proactive measures to improve public health outcomes and allocate resources more effectively before a crisis.
Q115. Which open-source software framework is traditionally most associated with the distributed storage and processing of massive Big Data sets?
Explanation
Apache Hadoop is a foundational open-source framework designed for the distributed storage and processing of exceptionally large datasets across clusters of computers. It utilizes the Hadoop Distributed File System for storage and MapReduce for processing, allowing for high scalability and fault tolerance. As a core tool in the Big Data ecosystem, Hadoop enables organizations to handle massive information volumes.
Q116. Select the correct order of the Data Analytics Maturity Model, progressing from basic hindsight to advanced foresight and optimization.
Explanation
The Data Analytics Maturity Model tracks the progression from understanding the past to optimizing the future. It begins with descriptive analytics to explain what happened, followed by diagnostic analytics to understand why. Predictive analytics then forecasts future trends, and finally, prescriptive analytics provides recommendations on how to achieve specific goals. This sequence represents increasing organizational value and technical complexity in data.
Q117. Identify the odd one out among the characteristics commonly known as the "V’s" of Big Data.
Explanation
The characteristics of Big Data are traditionally defined by several "Vs," including Velocity, Variety, and Veracity. These terms describe the speed, diversity, and reliability of information. While virtualization is a critical technology that supports cloud computing and data center management, it is not one of the fundamental V’s used to define Big Data itself. It remains a separate technological.
Q118. Which statement best defines "Data Mining"?
Explanation
Data mining is the process of discovering hidden patterns, correlations, and trends within large datasets using a variety of statistical and computational techniques. Its goal is to extract valuable information that can be used to make informed business decisions or scientific discoveries. By transforming raw data into meaningful knowledge, data mining allows organizations to predict behaviors and improve their overall operational.
Q119. Which of the following is not a technology or tool typically used for handling Big Data?
Explanation
Handling Big Data requires specialized tools like Apache Spark for fast processing, NoSQL databases for flexible storage, and HDFS for distributed file management. These technologies are designed to manage the unique challenges of scale and variety. Microsoft Paint, conversely, is a simple graphics editing application for individual images. It serves no function in the complex ecosystem of data.
Q120. What is a direct organizational effect of utilizing predictive analytics in business operations?
Explanation
Utilizing predictive analytics allows businesses to move beyond historical reporting to actively forecasting future trends and consumer behaviors. By identifying likely outcomes, organizations can optimize their marketing strategies, improve supply chain management, and mitigate potential risks. This forward-looking approach enhances decision-making and provides a competitive advantage by allowing companies to anticipate market changes before they occur in reality.Answer key for these questions
| Q | Correct answer |
|---|---|
| 111 | (a) Data size and storage magnitude |
| 112 | (d) I, II, III, IV, and V |
| 113 | (b) Big Data analytics is solely concerned with analyzing highly structured tabular data. |
| 114 | (a) Big Data Predictive Analytics |
| 115 | (c) Apache Hadoop |
| 116 | (d) Descriptive, Diagnostic, Predictive, Prescriptive |
| 117 | (c) Virtualization |
| 118 | (b) Discovery of patterns in large sets |
| 119 | (d) Microsoft Paint |
| 120 | (a) Forecasting trends and behaviors |
Key facts from Computers, Information and Communication Technology
- First-generation computers used vacuum tubes; the second generation moved to transistors.
- Analog computers work on continuous variable data rather than discrete binary data.
- The Control Unit directs and coordinates the operations of the CPU.
- Cache memory gives the fastest access to the CPU; the hierarchy from fastest to slowest is registers, cache, RAM and magnetic disk.
- Adding more RAM to a system with many page faults reduces its dependence on virtual memory.
- An SSD differs from an HDD by the use of NAND flash memory.
Frequently asked questions
How many RAS Prelims practice MCQs are there on Computers, Information and Communication Technology?
This page has 200 practice MCQs on Computers, Information and Communication Technology (Science and Technology). Each has the correct answer, and most have an explanation.
What was used in first-generation computers?
Vacuum tubes. They were large, used much power and produced heat. The second generation replaced them with transistors, which were smaller, faster and more reliable, and later generations moved to integrated circuits and microprocessors.
Which memory is fastest for the CPU?
Cache memory, apart from the registers inside the CPU. The memory hierarchy from fastest to slowest is registers, cache, RAM and magnetic disk, and capacity increases as speed falls.
How does an SSD differ from an HDD?
An SSD stores data in NAND flash memory with no moving parts, while a hard disk drive uses spinning magnetic platters. As a result an SSD is faster, quieter and more resistant to shock.