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Welcome to the Advanced Data Analytics for Business MCQs Page

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

Advanced Data Analytics for Business MCQs | Page 15 of 15

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Q141.
What expertise is typically required for building analytic solutions?

a.

Expertise in data storage

b.

Expertise in machine learning

c.

Expertise in real-time analytics

d.

Expertise in marketing

Discuss
Answer: (b).Expertise in machine learning Explanation:Building analytic solutions typically requires expertise in machine learning.
Q142.
What is the need for developing declarative platforms in analytics?

a.

To reduce data storage costs

b.

To improve historical data analysis

c.

To lower the entry barrier for creating analytics applications

d.

To speed up real-time analytics

Discuss
Answer: (c).To lower the entry barrier for creating analytics applications Explanation:Declarative platforms are needed to significantly reduce the entry barrier for creating analytics applications.
Q143.
What is one of the main challenges for enterprises in managing their public image through social media?

a.

Lack of available data

b.

Control over marketing message

c.

Economic constraints

d.

Resource scheduling

Discuss
Answer: (b).Control over marketing message Explanation:Enterprises face the challenge of no longer being in control of their marketing message on social media.
Q144.
To accomplish reputation management in the social media space, what type of decisions need to be made in real-time?

a.

Economic decisions

b.

Marketing decisions

c.

Strategic decisions

d.

Resource decisions

Discuss
Answer: (c).Strategic decisions Explanation:Reputation management in social media requires making strategic decisions in real-time.
Q145.
What is the key factor for offering valuable decision support in marketing and reputation through social media management solutions?

a.

Availability of analytic insights

b.

Use of natural language

c.

Quality of content

d.

Speed of content consumption

Discuss
Answer: (a).Availability of analytic insights Explanation:Valuable decision support in marketing and reputation through social media management solutions depends on the availability of analytic insights.
Q146.
How do recommendation engines for online or mobile applications use machine learning algorithms?

a.

To predict weather forecasts

b.

To learn user tastes and make recommendations

c.

To analyze social media content

d.

To create mobile apps

Discuss
Answer: (b).To learn user tastes and make recommendations Explanation:Recommendation engines use machine learning algorithms to learn user tastes and make recommendations.
Q147.
What is a strong indicator of the effectiveness of a recommendation engine for online or mobile applications?

a.

High user ratings

b.

Low data storage costs

c.

User engagement in forums

d.

Percentage of videos watched from recommendations

Discuss
Answer: (d).Percentage of videos watched from recommendations Explanation:The percentage of videos watched from recommendations is a strong indicator of the effectiveness of a recommendation engine.
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