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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 25 of 43

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Q241.
Which project allows experiments on interlinked cluster systems for Big Data solutions?

a.

OpenCirrus project

b.

Amazon AWS

c.

Microsoft Azure

d.

Google Drive

Discuss
Answer: (a).OpenCirrus project Explanation:The OpenCirrus project is an international test bed that allows experiments on interlinked cluster systems for Big Data solutions.
Q242.
What does the term "self-healing" refer to in the context of Big Data systems?

a.

The ability to automatically solve computational problems.

b.

The ability to prevent all failures.

c.

The need for constant human intervention in case of failure.

d.

The ability to detect problems but not solve them.

Discuss
Answer: (a).The ability to automatically solve computational problems. Explanation:In the context of Big Data systems, "self-healing" refers to the ability to automatically solve computational problems, database and storage failures, and architecture issues.
Q243.
How can a system achieve self-healing in the event of a server or node failure?

a.

By requiring human intervention to solve the problem.

b.

By shutting down the entire architecture to prevent further issues.

c.

By automatically redirecting work to other resources and taking the failed machine offline.

d.

By increasing the load on the failed machine to test its resilience.

Discuss
Answer: (c).By automatically redirecting work to other resources and taking the failed machine offline. Explanation:A system can achieve self-healing in the event of a server or node failure by automatically redirecting work to other resources and taking the failed machine offline.
Q244.
What happens when a node/storage fails in a cluster with self-healing capabilities?

a.

The cluster becomes more efficient.

b.

The cluster continues to operate without any impact.

c.

The cluster recreates the missing data from the damaged node.

d.

The cluster relies on human intervention to recover.

Discuss
Answer: (c).The cluster recreates the missing data from the damaged node. Explanation:When a node/storage fails in a cluster with self-healing capabilities, the cluster recreates the missing data from the damaged node, thus ensuring data recovery.
Q245.
What is the BASE property in the context of NoSQL databases?

a.

Basic Available, Soft state, and Eventual consistent

b.

Atomicity, Consistency, and Availability

c.

Consistency, Isolation, and Durability

d.

Resource Description Framework (RDF) and Eventual Consistency

Discuss
Answer: (a).Basic Available, Soft state, and Eventual consistent Explanation:In the context of NoSQL databases, the BASE property stands for Basic Available, Soft state, and Eventual consistent.
Q246.
What is the primary goal of the CAP theorem in distributed storage systems?

a.

To guarantee strong consistency in all situations

b.

To ensure high availability at all times

c.

To provide partition tolerance without compromises

d.

To highlight the trade-offs between key system attributes

Discuss
Answer: (d).To highlight the trade-offs between key system attributes Explanation:The primary goal of the CAP theorem in distributed storage systems is to highlight the trade-offs between key system attributes, including consistency, availability, and partition tolerance.
Q247.
Why is it challenging to implement a fault-tolerant BASE architecture for Big Data management?

a.

Because BASE architectures lack scalability

b.

Because BASE architectures require ACID properties

c.

Because of the trade-offs required by the CAP theorem

d.

Because BASE architectures are incompatible with NoSQL databases

Discuss
Answer: (c).Because of the trade-offs required by the CAP theorem Explanation:Implementing a fault-tolerant BASE architecture for Big Data management is challenging due to the trade-offs required by the CAP theorem, which balances consistency, availability, and partition tolerance.
Q248.
How does the CAP theorem impact the design of Big Data solutions?

a.

It mandates the use of specific data models.

b.

It necessitates the use of NoSQL databases.

c.

It requires a careful balance between system attributes.

d.

It enforces the use of ACID properties in all databases.

Discuss
Answer: (c).It requires a careful balance between system attributes. Explanation:The CAP theorem impacts the design of Big Data solutions by requiring a careful balance between system attributes, such as consistency, availability, and partition tolerance.
Q249.
What role does the concept of "eventual consistency" play in the CAP theorem?

a.

It ensures that all data is consistently available.

b.

It guarantees immediate data consistency.

c.

It describes a compromise between consistency and availability.

d.

It eliminates the need for partition tolerance.

Discuss
Answer: (c).It describes a compromise between consistency and availability. Explanation:The concept of "eventual consistency" in the CAP theorem describes a compromise between consistency and availability in distributed storage systems.
Q250.
What characterizes Big Data solutions in terms of data management aspects?

a.

Maximum database size and query complexity

b.

Data models and data flow rate

c.

Maximum data flow rate and query models

d.

Data partitioning and data query structure

Discuss
Answer: (b).Data models and data flow rate Explanation:Big Data solutions are characterized by data models, the capability of setting up distributed and clustered data management solutions, the sustainable data flow rate, and more.
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