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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 9 of 14

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Q81.
What is the primary motivation for using query rewriting techniques in the context of OBDA?

a.

To separate ontology reasoning from data reasoning.

b.

To eliminate the need for query optimization.

c.

To reduce the size of the ontology.

d.

To replace ontology reasoning with data reasoning.

Discuss
Answer: (a).To separate ontology reasoning from data reasoning. Explanation:The primary motivation for using query rewriting techniques in the context of OBDA is to separate ontology reasoning from data reasoning, which can be costly, especially in the presence of Big Data.
Q82.
What benefits can be achieved through load-time precomputation of inferences in OBDA systems?

a.

Faster query rewriting.

b.

Smaller query representations.

c.

Improved data storage.

d.

Semantic preservation.

Discuss
Answer: (a).Faster query rewriting. Explanation:Load-time precomputation of inferences in OBDA systems can lead to faster query rewriting.
Q83.
What role does the form of the SQL query play in achieving high performance in the context of query rewriting into SQL?

a.

It has no impact on performance.

b.

It guarantees high performance.

c.

It plays a critical role.

d.

It depends on the size of the ontology.

Discuss
Answer: (c).It plays a critical role. Explanation:The form of the SQL query, including the use of subqueries, views, nested expressions, etc., plays a critical role in achieving high performance in the context of query rewriting into SQL.
Q84.
Which system served as a platform for the implementation of epistemic-query answering techniques in the context of Description Logic (DL) ontologies?

a.

QuOnto

b.

Requiem

c.

Presto

d.

Quest

Discuss
Answer: (a).QuOnto Explanation:QuOnto served as a platform for the implementation of epistemic-query answering techniques in the context of Description Logic (DL) ontologies.
Q85.
Which prototype implemented resolution-based query rewriting techniques?

a.

QuOnto

b.

Requiem

c.

Presto

d.

Quest

Discuss
Answer: (b).Requiem Explanation:Requiem implemented resolution-based query rewriting techniques.
Q86.
Which system focused on the exploitation of efficient rewriting techniques, SQO optimization, and the generation of efficient SQL queries?

a.

QuOnto

b.

Requiem

c.

Presto

d.

Quest

Discuss
Answer: (d).Quest Explanation:Quest focused on the exploitation of efficient rewriting techniques, Semantic Query Optimization (SQO) optimization, as well as the generation of efficient SQL queries.
Q87.
What was the primary focus of QuOnto and Mastro initially?

a.

Performance of query rewriting algorithms

b.

Implementation of epistemic-query answering techniques

c.

Optimization of SQL queries

d.

Semantic data storage

Discuss
Answer: (b).Implementation of epistemic-query answering techniques Explanation:The primary focus of QuOnto and Mastro initially was the implementation of epistemic-query answering techniques.
Q88.
Which generation of OBDA systems has focused on efficient rewriting techniques, SQO optimization, and efficient SQL query generation?

a.

First generation

b.

Second generation

c.

Third generation

d.

Fourth generation

Discuss
Answer: (c).Third generation Explanation:The latest generation OBDA systems, including Quest and Prexto, have focused on efficient rewriting techniques, Semantic Query Optimization (SQO) optimization, and efficient SQL query generation.
Q89.
What is a challenge in optimizing query rewriting techniques for industrial applications and Big Data?

a.

Current techniques are too complex

b.

Limited support for constraints in optimization

c.

Lack of available query rewriting tools

d.

Inadequate query languages

Discuss
Answer: (b).Limited support for constraints in optimization Explanation:One challenge in optimizing query rewriting techniques for industrial applications and Big Data is the limited support for constraints in optimization.
Q90.
What has been the focus of optimization in the context of OBDA systems so far?

a.

Transforming data into ABox

b.

On-the-fly query rewriting

c.

Removal of constraints

d.

Incorporating domain-specific procedures

Discuss
Answer: (b).On-the-fly query rewriting Explanation:The focus of optimization in the context of OBDA systems has primarily been on on-the-fly query rewriting.
Page 9 of 14

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