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Welcome to the Big Data Exploration MCQs Page

Dive deep into the fascinating world of Big Data Exploration with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Data Exploration, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Data Exploration, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

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Check out the MCQs below to embark on an enriching journey through Big Data Exploration. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Big Data Exploration. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Data Exploration MCQs | Page 9 of 9

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Answer: (c).Past query-processing actions and data properties Explanation:In Sciborg, the creation of weighted samples is driven by past query-processing actions and the properties of the data, allowing for more relevant and informative sampling.
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Answer: (c).Response-time-based query processing Explanation:Sciborg prioritizes response-time-based query processing, aiming to provide quick responses to queries, especially in interactive scenarios.
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Answer: (b).It sacrifices correctness and completeness for improved response times. Explanation:The trade-off made in the Sciborg approach to query processing is that it sacrifices correctness and completeness in favor of improved response times, particularly in interactive query scenarios.
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Answer: (a).Seamless integration with cloud technology Explanation:One characteristic of the Blink project is its seamless integration with cloud technology, allowing it to scale to massive amounts of data and processing nodes.
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Answer: (c).Supporting query processing over samples as a core feature of the database system. Explanation:Both the Blink and Sciborg projects share the vision of creating database kernels where query processing over samples is a core feature of the system.
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Answer: (a).It focuses on achieving correctness and completeness at a lower level of database operators. Explanation:The one-minute database kernels approach differs from other exploration-based database kernel ideas by pushing the idea of sacrificing correctness for performance to a lower level, such as database operators, to achieve better control over performance parameters.
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Answer: (b).Providing quality guarantees for query results Explanation:One of the main challenges associated with the one-minute database kernels approach is providing quality guarantees for query results despite sacrificing correctness for performance.
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Answer: (b).Allowing users to visualize data and interact with it through touch-based gestures Explanation:The main idea behind the dbTouch project is to enable users to visualize data and interact with it through touch-based gestures, making data exploration more accessible.
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Answer: (c).dbTouch reacts instantly to touch-based interactions and processes data in response to user gestures. Explanation:dbTouch differs from traditional database systems in that it reacts instantly to touch-based interactions and processes data in response to user gestures, rather than requiring users to write complex SQL queries.
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Answer: (c).Reacting instantly to every touch and providing quick response times Explanation:One of the main challenges associated with dbTouch is designing database kernels that can react instantly to every touch and provide quick response times.
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