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Welcome to the Big Data Exploration MCQs Page

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

Big Data Exploration MCQs | Page 3 of 9

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Q21.
What is the purpose of adaptive indexing?

a.

To build indices on-the-fly as opposed to a priori

b.

To provide complete and correct answers to all queries

c.

To load all data in the system before processing queries

d.

To engage data analysts in a seamless way and avoid bottlenecks

Discuss
Answer: (a).To build indices on-the-fly as opposed to a priori Explanation:Adaptive indexing is used to build indices on-the-fly as opposed to a priori to support data exploration.
Q22.
What is the primary purpose of adaptive indexing?

a.

To create accelerator structures for databases

b.

To choose a subset of potential indices for databases

c.

To provide suggestions for index selection in database systems

d.

To address the challenges of dynamic Big Data environments in indexing

Discuss
Answer: (d).To address the challenges of dynamic Big Data environments in indexing Explanation:The primary purpose of adaptive indexing is to address the challenges of dynamic Big Data environments in indexing.
Q23.
What is the main problem with the set of potential indices in database systems?

a.

The potential indices are too small to be useful.

b.

The potential indices are not created properly.

c.

The potential indices are too large to be covered by default.

d.

The potential indices are not accessible to database administrators.

Discuss
Answer: (c).The potential indices are too large to be covered by default. Explanation:The main problem with the set of potential indices in database systems is that it is too large to be covered by default.
Q24.
How has the complexity of index selection in database systems changed over time?

a.

It has become simpler for human administration.

b.

It is now fully automated and does not require human involvement.

c.

It has become too complex for human administration alone.

d.

It has remained the same over the years.

Discuss
Answer: (c).It has become too complex for human administration alone. Explanation:The complexity of index selection in database systems has increased over time and has become too complex for human administration alone.
Q25.
What is required for offline indexing to make tuning choices and perform tuning actions?

a.

Workload knowledge and idle time

b.

Database administrator tools

c.

Accelerator structures

d.

Query-processing time

Discuss
Answer: (a).Workload knowledge and idle time Explanation:For offline indexing to make tuning choices and perform tuning actions, it requires workload knowledge and idle time.
Q26.
In dynamic environments with Big Data, what are described as scarce resources?

a.

Database administrators

b.

Query patterns

c.

Workload knowledge and idle time

d.

Scientific databases

Discuss
Answer: (c).Workload knowledge and idle time Explanation:In dynamic environments with Big Data, workload knowledge and idle time are described as scarce resources.
Q27.
What approach was introduced to address the physical design problem in databases?

a.

Cracking

b.

Tuning advisor

c.

Workload analysis

d.

Index optimization

Discuss
Answer: (a).Cracking Explanation:The approach introduced to address the physical design problem in databases is called "cracking."
Q28.
How does database cracking differ from traditional indexing in terms of index creation?

a.

Database cracking creates all possible indices upfront.

b.

Database cracking creates indices incrementally during query processing.

c.

Database cracking relies on workload knowledge to create indices.

d.

Database cracking requires idle time for index creation.

Discuss
Answer: (b).Database cracking creates indices incrementally during query processing. Explanation:Database cracking creates indices incrementally during query processing, as opposed to creating all possible indices upfront.
Q29.
How is every query treated in the context of database cracking?

a.

As a hint on how to optimize query processing

b.

As a workload analysis request

c.

As an index creation task

d.

As a fully formed query that must be processed immediately

Discuss
Answer: (a).As a hint on how to optimize query processing Explanation:In the context of database cracking, every query is treated as a hint on how data should be stored and organized to optimize query processing.
Q30.
What is the primary data storage representation used in column-store databases?

a.

Variable-width sparse arrays

b.

Fixed-width dense arrays

c.

Row-based storage

d.

Unstructured data storage

Discuss
Answer: (b).Fixed-width dense arrays Explanation:Column-store databases primarily use fixed-width dense arrays as their data storage representation.
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