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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 12 of 14

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Discuss
Answer: (c).A much faster "native" implementation Explanation:CQELS is known for being a much faster "native" implementation of stream-based queries.
Discuss
Answer: (b).By translating queries to plain SQL Explanation:T-SPARQL applies techniques from temporal databases to RDF querying by translating queries to plain SQL.
Q113.
What combination of querying approaches is considered useful in applications?
Discuss
Answer: (b).Stream-based, temporal, and static Explanation:A combination of stream-based (or window-based), temporal (history-based), and static querying is useful in applications.
Q114.
What was a design goal in early work on deductive event recognition?
Discuss
Answer: (c).Semantics Explanation:Scalability was not the design goal of early work on deductive event recognition.
Q115.
Why are brute-force approaches for query answering with respect to ontologies not suitable for large real-world ontologies?
Discuss
Answer: (a).Because they require materialization Explanation:Brute-force approaches such as materialization are not suitable for large real-world ontologies.
Q116.
What is one promising idea for scalable stream-based answering of continuous queries?
Discuss
Answer: (c).Using an ontology and mapping rules Explanation:One promising idea is to use an ontology and mapping rules for scalable stream-based answering of continuous queries.
Discuss
Answer: (c).Centralized query execution using a single database system Explanation:OBDA approaches traditionally assumed centralized query execution using a single database system.
Discuss
Answer: (b).Because data are distributed over many autonomous, heterogeneous sources Explanation:In the real world, data for OBDA are often distributed over many autonomous, heterogeneous sources, making the assumption of centralized query execution impractical.
Discuss
Answer: (b).A federated query processing engine Explanation:FedX is described as a federated query processing engine.
Q120.
What is one of the important advantages of cloud computing, especially IaaS, PaaS, and SaaS clouds?
Discuss
Answer: (b).Low operational cost Explanation:One of the important advantages of cloud computing, especially IaaS, PaaS, and SaaS clouds, is the ability to lease resources only for as long as needed, based on a per quantum pricing scheme, with low operational cost.

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