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Welcome to the Big Data Challenges and Opportunities MCQs Page

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

Big Data Challenges and Opportunities MCQs | Page 4 of 6

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Q31.
What advantage does Hadoop offer in terms of job recovery when tasks fail?

a.

Automatic and incremental backward-recovery

b.

Ability to recover from task failures without intervention

c.

Support for GUI-based job recovery

d.

Requirement for schema-first approach

Discuss
Answer: (b).Ability to recover from task failures without intervention Explanation:Hadoop offers the advantage of automatic and incremental forward-recovery of jobs with failed tasks, allowing recovery from task failures without manual intervention.
Q32.
What is one of the benefits of Hadoop's ability to schedule very large jobs in smaller chunks?

a.

Faster execution of individual tasks

b.

Reduced reliance on machine learning algorithms

c.

Improved data placement and rebalancing

d.

Support for exploratory analysis of data

Discuss
Answer: (a).Faster execution of individual tasks Explanation:One of the benefits of Hadoop's ability to schedule very large jobs in smaller chunks is faster execution of individual tasks.
Q33.
What opportunity does the combination of Hadoop's scale, unstructured data processing, and machine learning algorithms create?

a.

The opportunity to build new game-changing applications

b.

The need for schema-first data processing

c.

The ability to handle structured data efficiently

d.

The requirement for extensive manual data placement and rebalancing

Discuss
Answer: (a).The opportunity to build new game-changing applications Explanation:The combination of Hadoop's scale, unstructured data processing, and machine learning algorithms creates the opportunity to build new game-changing applications.
Q34.
What is one of the challenges associated with the Hadoop file system (HDS)?

a.

Lack of support for batch loading and unloading of data

b.

Centralized metadata store (NameNode) that is a single point of failure

c.

Inability to account for real-time changes

d.

Limited checkpointing at intermediate stages

Discuss
Answer: (b).Centralized metadata store (NameNode) that is a single point of failure Explanation:One of the challenges associated with the Hadoop file system (HDS) is its centralized metadata store (NameNode), which represents a single point of failure without availability.
Q35.
What does the batch-oriented nature of Hadoop mean for real-time changes?

a.

Hadoop can efficiently handle real-time changes in data.

b.

Hadoop lacks the capability to process real-time changes.

c.

Hadoop relies on transactional guarantees for real-time changes.

d.

Hadoop requires specialized software for real-time changes.

Discuss
Answer: (b).Hadoop lacks the capability to process real-time changes. Explanation:The batch-oriented nature of Hadoop means that it lacks the capability to account for real-time changes in data.
Q36.
What is a major limitation of Hadoop's approach to data reliability?

a.

Hadoop relies on centralized metadata stores.

b.

Hadoop provides transactional guarantees for all jobs.

c.

Hadoop's checkpointing mechanism does not guarantee transactional consistency.

d.

Hadoop ensures seamless integration of data processing platforms.

Discuss
Answer: (c).Hadoop's checkpointing mechanism does not guarantee transactional consistency. Explanation:Hadoop assumes that workloads will belong running and relies on checkpointing at intermediate stages, which means parts of a job can fail and be restarted without transactional guarantees.
Q37.
What challenge is associated with getting data in and out of Hadoop?

a.

The need for specialized data formats

b.

Limited support for real-time data transfer

c.

The append-only nature of the Hadoop Distributed File System (HDFS)

d.

Incompatibility with SQL databases

Discuss
Answer: (c).The append-only nature of the Hadoop Distributed File System (HDFS) Explanation:Getting data in and out of Hadoop is challenged by the append-only nature of the Hadoop Distributed File System (HDFS), which requires programs to batch load and unload data into a cluster.
Q38.
What is one example of a technological solution for Big Data Analytics that involves both NoSQL and Hadoop?

a.

Using a NoSQL data store as a front end for real-time processing and Hadoop for batch processing

b.

Using a NewSQL data store for real-time analytics

c.

Using a relational database as the primary data store

d.

Using a standalone Hadoop cluster for all analytics tasks

Discuss
Answer: (a).Using a NoSQL data store as a front end for real-time processing and Hadoop for batch processing Explanation:The example involves using a NoSQL data store as a front end for real-time processing and Hadoop for batch processing in Big Data Analytics.
Q39.
What is the primary interface in the solution where NoSQL and Hadoop are combined?

a.

Hadoop layer

b.

Real-time data store

c.

Batch processing

d.

Data connector

Discuss
Answer: (b).Real-time data store Explanation:In the solution combining NoSQL and Hadoop, the primary interface is typically via the real-time data store, with Hadoop serving as the backend for batch processing.
Q40.
What is one advantage of Vertica's execution engine optimization?

a.

It provides real-time analytics on Big Data.

b.

It loads data faster than any other database.

c.

It uses a hybrid in-memory/disk architecture.

d.

It supports materialized views and indices.

Discuss
Answer: (a).It provides real-time analytics on Big Data. Explanation:Vertica's execution engine optimization allows for real-time analytics on Big Data, which is one of its advantages.
Page 4 of 6

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