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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 1 of 14

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Q1.
What is the primary challenge faced by end-users when dealing with Big Data?

a.

Lack of IT personnel

b.

Slow network connections

c.

Difficulty in formulating queries

d.

Data access challenges

Discuss
Answer: (d).Data access challenges Explanation:The main problem faced by end-users when dealing with Big Data is data access challenges.
Q2.
Why do end-users often experience a significant turnaround time for information requests in the context of Big Data?

a.

Lack of IT expertise

b.

Slow computers

c.

Limited data sources

d.

High costs

Discuss
Answer: (a).Lack of IT expertise Explanation:End-users face extended turnaround times due to their dependence on IT experts, primarily because of the lack of IT expertise.
Q3.
How are end-user queries transformed when utilizing ontology-based data access (OBDA)?

a.

They are rewritten in a different language

b.

They are converted into natural language

c.

They are mapped to concepts in an ontology

d.

They are discarded

Discuss
Answer: (c).They are mapped to concepts in an ontology Explanation:End-user queries are mapped to concepts in an ontology when using ontology-based data access (OBDA).
Q4.
What are some of the challenges associated with ontology-based data access (OBDA) in the context of Big Data?

a.

Lack of interest from IT experts

b.

Inadequate ontology modeling

c.

High costs of implementation

d.

Low demand for data access

Discuss
Answer: (b).Inadequate ontology modeling Explanation:Inadequate ontology modeling is one of the challenges associated with ontology-based data access (OBDA) in Big Data scenarios.
Q5.
What is the expected outcome of raising OBDA to a level where it can be successfully deployed to Big Data?

a.

Reduced IT involvement

b.

Faster information requests

c.

Lower data complexity

d.

Improved data security

Discuss
Answer: (b).Faster information requests Explanation:Raising OBDA to a successful level in Big Data scenarios is expected to result in faster information requests.
Q6.
What characterizes the data in knowledge- and data-intensive enterprises?

a.

Small amounts of data

b.

Structured data only

c.

Massive amounts of data

d.

Data from a single source

Discuss
Answer: (c).Massive amounts of data Explanation:Knowledge- and data-intensive enterprises deal with massive amounts of data.
Q7.
How do end-users typically interact with data in these enterprises?

a.

They directly manipulate raw data.

b.

They use specialized applications with predefined queries.

c.

They perform manual data entry.

d.

They build custom databases.

Discuss
Answer: (b).They use specialized applications with predefined queries. Explanation:End-users operate on data using specialized applications with predefined queries.
Q8.
What is the turnaround time like when end-users use predefined queries in specialized applications?

a.

Seconds or less

b.

Minutes to hours

c.

Days to weeks

d.

Months to years

Discuss
Answer: (a).Seconds or less Explanation:When predefined queries are used, the turnaround time can be in the range of seconds or minutes.
Q9.
Why are situations where users need to explore data with ad hoc queries more challenging?

a.

Because the data is not available

b.

Because IT-skilled personnel are not available

c.

Because it requires domain knowledge

d.

Because ad hoc queries are slow

Discuss
Answer: (c).Because it requires domain knowledge Explanation:Exploring data with ad hoc queries is challenging because it requires in-depth knowledge of the domain.
Q10.
How does the involvement of IT-skilled personnel affect the turnaround time in exploring data with ad hoc queries?

a.

It speeds up the turnaround time.

b.

It has no impact on the turnaround time.

c.

It depends on the complexity of the query.

d.

It increases the turnaround time.

Discuss
Answer: (d).It increases the turnaround time. Explanation:Involving IT-skilled personnel in developing ad hoc queries can result in longer turnaround times.
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