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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 5 of 6

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Q41.
How does Vertica handle data loading to address the myth that columnar databases are slow to load?

a.

It uses an entirely in-memory load architecture.

b.

It loads data into a write-optimized row store and then converts it to read-optimized sorted columnar storage in the background.

c.

It relies on auxiliary data structures like indices and materialized views for fast loading.

d.

It requires frequent downtime for data loading.

Discuss
Answer: (b).It loads data into a write-optimized row store and then converts it to read-optimized sorted columnar storage in the background. Explanation:Vertica addresses the myth that columnar databases are slow to load by loading data into a write-optimized row store and then converting it to read-optimized sorted columnar storage in the background.
Q42.
What is one feature that distinguishes Vertica from traditional databases in terms of operations and downtime?

a.

Vertica supports distributed transactions.

b.

Vertica has a high-cost licensing model.

c.

Many operations that cause downtime in traditional databases are online in Vertica.

d.

Vertica requires frequent manual tuning for performance.

Discuss
Answer: (c).Many operations that cause downtime in traditional databases are online in Vertica. Explanation:One feature that distinguishes Vertica from traditional databases is that many operations that cause downtime in traditional databases are online in Vertica, including adding or upgrading nodes, adding or modifying database objects, etc.
Q43.
What is one of the barriers that Vertica aims to remove when it comes to monetizing Big Data?

a.

Complex data modeling requirements

b.

High-cost hardware infrastructure

c.

Slow query performance

d.

Limited data storage capacity

Discuss
Answer: (c).Slow query performance Explanation:One of the barriers that Vertica aims to remove when it comes to monetizing Big Data is slow query performance.
Q44.
What is one of the main differences between Vertica and Hadoop in terms of data access and storage?

a.

Vertica uses a distributed file system for data storage, while Hadoop relies on a relational database.

b.

Vertica's interface is SQL, and data must be loaded into an SQL schema for analysis, while Hadoop loads data AS IS into a distributed file system and is accessed programmatically.

c.

Vertica is designed for OLTP workloads, while Hadoop is designed for analytics.

d.

Vertica provides transaction guarantees, while Hadoop does not.

Discuss
Answer: (b).Vertica's interface is SQL, and data must be loaded into an SQL schema for analysis, while Hadoop loads data AS IS into a distributed file system and is accessed programmatically. Explanation:One of the main differences between Vertica and Hadoop is that Vertica's interface is SQL, and data must be designed and loaded into an SQL schema for analysis, while Hadoop loads data AS IS into a distributed file system and is accessed programmatically.
Q45.
What is the primary interface for accessing data in Vertica?

a.

MapReduce programs

b.

SQL

c.

PigLatin

d.

HiveQL

Discuss
Answer: (b).SQL Explanation:The primary interface for accessing data in Vertica is SQL.
Q46.
What is the intended use case for VoltDB in conjunction with a companion data warehouse?

a.

Running complex joins for OLAP workloads

b.

Running big reads for OLTP workloads

c.

Storing and managing petabyte-sized data warehouses

d.

Eliminating the need for a Hadoop connector

Discuss
Answer: (b).Running big reads for OLTP workloads Explanation:VoltDB is intended for the OLTP portion, and some customers wish to run Hadoop as a data warehouse platform. In this architecture, big reads are directed to a companion data warehouse.
Q47.
What is the primary focus of Hadapt in the context of analytics?

a.

OLTP workloads

b.

Real-time data processing

c.

Complementing Hadoop for analytics

d.

Data warehousing

Discuss
Answer: (c).Complementing Hadoop for analytics Explanation:Hadapt is primarily focused on complementing Hadoop for analytics in the context of Big Data.
Q48.
What has Hadapt brought to Hadoop in order to make it more accessible to mainstream users?

a.

Real-time data processing capabilities

b.

Advanced data visualization tools

c.

High-performance SQL interface and features like indexing and SQL access

d.

Enhanced data encryption

Discuss
Answer: (c).High-performance SQL interface and features like indexing and SQL access Explanation:Hadapt has added a high-performance SQL interface and features like indexing, copartitioned joins, broadcast joins, and SQL access to Hadoop, making it more accessible to mainstream users.
Q49.
What is one of the advantages of the Hadapt architecture?

a.

It requires multiple clusters for structured and unstructured data.

b.

It is optimized only for structured data.

c.

It can only be used by data scientists.

d.

It allows both relational and non-relational data to be included in the same query.

Discuss
Answer: (d).It allows both relational and non-relational data to be included in the same query. Explanation:One of the advantages of the Hadapt architecture is that it allows both relational data and non-relational data to be included in the same query, enabling new types of analytics involving both structured and unstructured data.
Q50.
Which organization published a white paper entitled "Big Data, Big Impact: New Possibilities for International Development" at the 2012 Annual Meeting in Davos?

a.

United Nations Global Pulse Initiative

b.

DataKind

c.

World Economic Forum

d.

Viral Forecasting

Discuss
Answer: (c).World Economic Forum Explanation:The World Economic Forum (WEF) published a white paper entitled "Big Data, Big Impact: New Possibilities for International Development" at the 2012 Annual Meeting in Davos.
Page 5 of 6

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