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Welcome to the Scalable End User Access to Big Data MCQs Page

Dive deep into the fascinating world of Scalable End User Access to Big Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Scalable End User Access to Big Data, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Scalable End User Access to Big Data, 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 Scalable End User Access to Big Data. 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 Scalable End User Access to Big Data. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Scalable End User Access to Big Data MCQs | Page 13 of 14

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Q121.
Which computing paradigm is the most popular for Big Data processing?
Discuss
Answer: (c).MapReduce Explanation:The most popular computing paradigm for Big Data processing is MapReduce.
Discuss
Answer: (a).Running queries with maximum parallelism Explanation:The main advantage of elastic query execution is the ability to run queries with the maximum amount of parallelism at each stage of execution.
Discuss
Answer: (c).A massively parallel query execution system for Big Data Explanation:The Athena Distributed Processing (ADP) system is a massively parallel query execution system for Big Data.
Q124.
What kind of queries are expressed in the data flow language used by ADP?
Discuss
Answer: (b).Queries with complex graphs Explanation:The data flow language used by ADP allows for expressing queries with complex graphs with operators as nodes and producer-consumer relationships as edges.
Discuss
Answer: (c).Ability to insulate applications from execution environments Explanation:One of the key features of ADP is its ability to insulate applications from execution environments, including local clusters, private clouds, or public clouds.
Discuss
Answer: (b).Allocating and reserving resources for query execution Explanation:ARM (ADP Resource Mediator) is responsible for allocating and reserving resources needed for query execution.
Discuss
Answer: (b).Abstracting physical and virtual machine details Explanation:Containers are used in the ADP system to abstract from the details of a physical machine in a cluster or a virtual machine in a cloud.
Q128.
Which services have been developed on top of ADP that are useful for the OBDA paradigm?
Discuss
Answer: (a).AdpDB and AdpMR Explanation:AdpDB (SQL engine) and AdpMR (MapReduce engine) have been developed on top of ADP and are useful for the OBDA paradigm.
Discuss
Answer: (d).By integrating tightly with query transformation modules and supporting federation, continuous, and temporal queries Explanation:ADP will be extended to maximize its impact on the OBDA Big Data paradigm by integrating tightly with query transformation modules, supporting federation, continuous, and temporal queries, among other enhancements.
Q130.
What is a major bottleneck in data-intensive industries when it comes to giving end-users flexible access to large corporate data stores?
Discuss
Answer: (c).Dependency on highly skilled IT experts for query access Explanation:A major bottleneck is the dependency on highly skilled IT experts for query access beyond predefined queries.

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