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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 10 of 14

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Q91.
In the context of Big Data and complex analytical queries, what is considered a viable alternative for achieving good performance?

a.

Declarative OBDA

b.

Pure on-the-fly query rewriting

c.

Hybrid approaches

d.

Domain-specific procedures

Discuss
Answer: (c).Hybrid approaches Explanation:In the context of Big Data and complex analytical queries, hybrid approaches are considered a viable alternative for achieving good performance.
Q92.
What should an OBDA system provide when domain-specific procedures are more efficient?

a.

Enhanced reasoning capabilities

b.

Complex query languages

c.

Integration with external databases

d.

Means to define domain-specific procedures

Discuss
Answer: (d).Means to define domain-specific procedures Explanation:An OBDA system should provide the means to define domain-specific procedures when they are more efficient.
Q93.
What should an optimal system for query answering in the context of Big Data include?

a.

A focus on declarative OBDA

b.

A single optimization technique

c.

An integral approach handling various aspects of query answering

d.

Removal of reasoning with respect to the ontology

Discuss
Answer: (c).An integral approach handling various aspects of query answering Explanation:An optimal system for query answering in the context of Big Data should include an integral approach handling various aspects of query answering, optimization, and reasoning with respect to the data and ontology.
Q94.
In the context of OBDA solutions for industrial applications involving time-stamped data, what is one of the requirements for the user query language?

a.

Support for complex ontology modeling

b.

Support for temporal references and combinations with ontology concepts

c.

Support for geographical queries

d.

Support for data validation

Discuss
Answer: (b).Support for temporal references and combinations with ontology concepts Explanation:One of the requirements for the user query language in the context of OBDA solutions for industrial applications involving time-stamped data is support for temporal references and combinations with ontology concepts.
Q95.
What is a challenge when using SQL to represent validity of facts with attributes Start and End?

a.

SQL lacks built-in data types for times and dates

b.

SQL queries become too simple

c.

Complex SQL queries are required for certain operations

d.

SQL cannot handle geographical data

Discuss
Answer: (c).Complex SQL queries are required for certain operations Explanation:When representing validity of facts with attributes Start and End in SQL, complex SQL queries are required for certain operations, such as maximizing validity intervals.
Q96.
What is the challenge when only irregular time points are stored for measurements?

a.

Complex SQL queries are needed

b.

The need for geographical data representation

c.

The problem of finding the next measurement of a sensor

d.

No challenge, irregular time points simplify queries

Discuss
Answer: (a).Complex SQL queries are needed Explanation:When only irregular time points are stored for measurements, complex SQL queries are needed for operations like finding the next measurement of a sensor and maximizing validity intervals.
Q97.
What attribute might refer to the insertion time (transaction time) of a tuple in a time-stamped data scenario?

a.

Start

b.

End

c.

Location

d.

Timepoint

Discuss
Answer: (d).Timepoint Explanation:In a time-stamped data scenario, the attribute "Timepoint" might refer to the insertion time (transaction time) of a tuple.
Q98.
What is a potential issue when querying for data in a Big Data scenario with mobile sensors?

a.

Difficulty in storing data efficiently

b.

Challenges related to query optimization

c.

The need for specialized database systems

d.

Lack of support for geographical data

Discuss
Answer: (b).Challenges related to query optimization Explanation:In a Big Data scenario with mobile sensors, one potential issue is challenges related to query optimization when querying for data that involves changes in location and time-stamps.
Q99.
What is the primary distinction between abstract temporal databases and concrete temporal databases?

a.

Abstract temporal databases rely on finite representations, while concrete temporal databases rely on infinite structures.

b.

Abstract temporal databases represent time using intervals, while concrete temporal databases use timestamps.

c.

Abstract temporal databases capture real-world time, while concrete temporal databases capture database storage time.

d.

Abstract temporal databases store data in the form of tables, while concrete temporal databases use compact representations.

Discuss
Answer: (c).Abstract temporal databases capture real-world time, while concrete temporal databases capture database storage time. Explanation:The primary distinction between abstract temporal databases and concrete temporal databases is that abstract temporal databases capture real-world time (valid time), while concrete temporal databases capture the time when data is stored in the database (transaction time).
Q100.
What is the difference between valid time and transaction time in the context of temporal databases?

a.

Valid time captures when data is stored in the database, while transaction time captures the time period during which a fact is considered true.

b.

Valid time captures the time period during which a fact is considered true, while transaction time captures when data is stored in the database.

c.

Valid time and transaction time are synonymous and can be used interchangeably.

d.

Valid time refers to past events, while transaction time refers to future events.

Discuss
Answer: (b).Valid time captures the time period during which a fact is considered true, while transaction time captures when data is stored in the database. Explanation:In the context of temporal databases, valid time captures the time period during which a fact is considered true, while transaction time captures when data is stored in the database.
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