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Welcome to the Introduction to Big Data MCQs Page

Dive deep into the fascinating world of Introduction to Big Data with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Introduction 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 Introduction 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 Introduction 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 Introduction to Big Data. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Introduction to Big Data MCQs | Page 26 of 43

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Q251.
What is the trend in data structures for Big Data?

a.

Exclusive use of relational databases

b.

Exclusive use of NoSQL databases

c.

Use of both relational and NoSQL databases

d.

Use of resource description framework (RDF) databases

Discuss
Answer: (c).Use of both relational and NoSQL databases Explanation:The trend in data structures for Big Data involves using both relational and NoSQL databases, as different situations and types of data may require different solutions.
Q252.
According to the CAP Theorem, what are the three main features that a distributed storage system can provide?

a.

Atomicity, Consistency, and Availability

b.

Consistency, Availability, and Partition Tolerance

c.

Consistency, Isolation, and Durability

d.

Atomicity, Partition Tolerance, and Eventual Consistency

Discuss
Answer: (b).Consistency, Availability, and Partition Tolerance Explanation:According to the CAP Theorem, a distributed storage system can provide only two of the three main features: Consistency, Availability, and Partition Tolerance.
Q253.
What does the property of "consistency" mean in the context of the CAP Theorem?

a.

The system provides the same data to all its clients after an operation.

b.

The solution is robust with respect to internal failures.

c.

The system continues to provide service even when divided into disconnected subsets.

d.

The system remains eventually consistent over time.

Discuss
Answer: (a).The system provides the same data to all its clients after an operation. Explanation:In the context of the CAP Theorem, the property of "consistency" means that a data model provides the same data to all its clients after an operation.
Q254.
How does the CAP Theorem impact Big Data solutions?

a.

It necessitates the use of only relational databases.

b.

It requires a compromise between availability and consistency.

c.

It guarantees full consistency in all distributed systems.

d.

It enforces the use of ACID properties in NoSQL databases.

Discuss
Answer: (b).It requires a compromise between availability and consistency. Explanation:The CAP Theorem impacts Big Data solutions by requiring a compromise between availability and consistency in distributed storage systems.
Q255.
What properties are described by the ACID paradigm in relational databases?

a.

Basic Available, Soft state, and Eventual consistent

b.

Atomicity, Consistency, Isolation, and Durability

c.

Consistency, Availability, and Partition Tolerance

d.

Resource Description Framework (RDF) and Eventual Consistency

Discuss
Answer: (b).Atomicity, Consistency, Isolation, and Durability Explanation:The ACID paradigm in relational databases describes the properties of Atomicity, Consistency, Isolation, and Durability in database transactions.
Q256.
What is one of the primary challenges associated with the database size in Big Data problems?

a.

Managing complex data relationships

b.

Handling high-speed data streams

c.

Ensuring data consistency

d.

Coping with huge data volumes

Discuss
Answer: (d).Coping with huge data volumes Explanation:One of the primary challenges associated with the database size in Big Data problems is coping with huge data volumes, which can reach magnitudes like hundreds of Tera Byte (TB), Peta Byte (PB), or Exa Byte (EB).
Q257.
Which factor significantly influences the performance of data management in Big Data solutions?

a.

Data indexing

b.

Data modeling

c.

Data relationships

d.

Data consistency

Discuss
Answer: (a).Data indexing Explanation:In Big Data solutions, data indexing significantly influences the performance of data management, often representing the bottleneck of data processing.
Q258.
What is the main factor that influences the performance of data management in terms of data modeling?

a.

The complexity of data relationships

b.

The type of data storage used

c.

The size of the database

d.

The choice of NoSQL database

Discuss
Answer: (a).The complexity of data relationships Explanation:The main factor that influences the performance of data management in terms of data modeling is the complexity of data relationships.
Q259.
Which category of NoSQL databases is known for its effectiveness in handling time series data and data from multiple sources?

a.

Key-value stores

b.

Wide column stores

c.

Document stores

d.

Graph databases

Discuss
Answer: (b).Wide column stores Explanation:Wide column stores (big tables) are known for their effectiveness in handling time series data and data from multiple sources, such as sensors, devices, and websites, needing high speed.
Q260.
In which situation are document stores like MongoDB and CouchDB typically used?

a.

When dealing with complex data relationships

b.

When handling large lists of elements

c.

When managing geospatial data

d.

When data are hardly representable with a relational model

Discuss
Answer: (d).When data are hardly representable with a relational model Explanation:Document stores like MongoDB and CouchDB are typically used when data are hardly representable with a relational model due to high complexity, such as with medical records or data from social networks.
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