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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 4 of 15

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Q31.
At what stage does predictive analytics come into play in relation to descriptive analytics?

a.

Predictive analytics precedes descriptive analytics

b.

Predictive analytics follows descriptive analytics

c.

Predictive analytics is unrelated to descriptive analytics

d.

Predictive analytics and descriptive analytics are the same thing

Discuss
Answer: (b).Predictive analytics follows descriptive analytics Explanation:Predictive analytics comes into play after descriptive analytics.
Q32.
What kind of data is used to detect hidden patterns and predict behavior in predictive analytics?

a.

Demographic data

b.

Transaction data

c.

Social data

d.

All of the above

Discuss
Answer: (d).All of the above Explanation:Predictive analytics uses a variety of data types, including demographic data, transaction data, and social data, to detect hidden patterns and predict behavior.
Q33.
Which techniques are commonly used in prescriptive analytics?

a.

Data mining and predictive modeling

b.

Optimization and simulation

c.

Clustering and decision trees

d.

Neural networks and regression analysis

Discuss
Answer: (b).Optimization and simulation Explanation:Optimization and simulation techniques are commonly used in prescriptive analytics.
Q34.
What is the concept of optimization divided into in prescriptive analytics?

a.

Optimization and data analysis

b.

Optimization and visualization

c.

Optimization and stochastic optimization

d.

Optimization and pattern recognition

Discuss
Answer: (c).Optimization and stochastic optimization Explanation:The concept of optimization in prescriptive analytics is divided into "Optimization" and "Stochastic optimization."
Q35.
What does stochastic optimization in prescriptive analytics aim to address?

a.

Achieving the best results

b.

Tackling improbabilities in the data

c.

Analyzing historical data

d.

Detecting patterns in real-time data

Discuss
Answer: (b).Tackling improbabilities in the data Explanation:Stochastic optimization in prescriptive analytics aims to address tackling improbabilities in the data to make better decisions.
Q36.
How are product recommendation engines like Amazon and Netflix typically managed?

a.

Manually by data scientists

b.

Through an automated workflow

c.

By monitoring high-level model results

d.

By updating recommendations on the website

Discuss
Answer: (b).Through an automated workflow Explanation:Product recommendation engines like Amazon and Netflix are typically managed through an automated workflow.
Q37.
What role does Big Data play in making the Big Data trend productive?

a.

It provides historical data for analysis

b.

It helps in identifying hidden patterns

c.

It combines with Prescriptive Analytics to increase productivity

d.

It relies on conventional techniques for processing

Discuss
Answer: (c).It combines with Prescriptive Analytics to increase productivity Explanation:Big Data, combined with Prescriptive Analytics, is how the Big Data trend becomes productive.
Q38.
What is the value of Big Data in analytics?

a.

It provides data on a real-time streaming basis

b.

It contains patterns hidden inside them

c.

It relies on conventional techniques

d.

It requires limited computing power

Discuss
Answer: (b).It contains patterns hidden inside them Explanation:The value of Big Data in analytics lies in the patterns hidden inside them.
Q39.
What is the primary requirement for some business applications that need to process huge amounts of data on a real-time streaming basis?

a.

Special-purpose computing hardware

b.

Conventional techniques

c.

Static data processing

d.

Data visualization tools

Discuss
Answer: (a).Special-purpose computing hardware Explanation:Some business applications that need to process huge amounts of data on a real-time streaming basis require special-purpose computing hardware.
Q40.
What is the growing necessity in data processing solutions?

a.

Batch type analysis

b.

Conventional techniques

c.

Interactive analysis

d.

MapReduce processing

Discuss
Answer: (c).Interactive analysis Explanation:The growing necessity in data processing solutions is interactive analysis.
Page 4 of 15

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