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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 5 of 15

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Q41.
What is the primary use of MapReduce in many organizations?

a.

Real-time data processing

b.

Interactive query processing

c.

Handling large-scale data processing needs

d.

Managing relational databases

Discuss
Answer: (c).Handling large-scale data processing needs Explanation:MapReduce is primarily used in many organizations to handle their large-scale data processing needs.
Q42.
What is the key characteristic of new MapReduce workloads?

a.

They focus on real-time data processing.

b.

They feature many small, short, and interactive jobs.

c.

They rely on relational databases.

d.

They target purely batch computations.

Discuss
Answer: (b).They feature many small, short, and interactive jobs. Explanation:New MapReduce workloads feature many small, short, and interactive jobs.
Q43.
What is the similarity between new MapReduce workloads and large-scale interactive query processing?

a.

They both target purely batch computations.

b.

They rely on relational databases.

c.

They share semantic similarities.

d.

They feature long and complex data processing jobs.

Discuss
Answer: (c).They share semantic similarities. Explanation:New MapReduce workloads share semantic similarities with large-scale interactive query processing.
Q44.
What has brought considerable benefit to MapReduce?

a.

The integration of query optimization techniques

b.

The adoption of purely batch computations

c.

The use of parallel databases

d.

The focus on real-time data processing

Discuss
Answer: (a).The integration of query optimization techniques Explanation:The integration of query optimization techniques into MapReduce has brought considerable benefit.
Q45.
What is the challenge when integrating query-like programming extensions into business-critical systems?

a.

Lack of technology companies

b.

Limited knowledge of real-life production workloads

c.

Excessive configuration tuning

d.

Incompatibility with MapReduce

Discuss
Answer: (b).Limited knowledge of real-life production workloads Explanation:Integrating query-like programming extensions into business-critical systems requires performance benchmarking against real-life production MapReduce workloads, and knowledge of such workloads is currently limited to a handful of technology companies.
Q46.
What is the primary characteristic of the original MapReduce use case?

a.

Handling interactive queries

b.

Real-time data processing

c.

Batch computations

d.

Short and interactive jobs

Discuss
Answer: (c).Batch computations Explanation:The original MapReduce use case primarily targeted purely batch computations.
Q47.
What kind of jobs do new MapReduce workloads feature?

a.

Long and complex jobs

b.

Real-time data processing jobs

c.

Interactive jobs

d.

Batch computation jobs

Discuss
Answer: (c).Interactive jobs Explanation:New MapReduce workloads feature many small, short, and interactive jobs.
Q48.
What do new query-like programming extensions aim to achieve for MapReduce?

a.

Real-time data processing

b.

Integration with parallel databases

c.

Enhanced batch computation capabilities

d.

Improved performance for interactive jobs

Discuss
Answer: (d).Improved performance for interactive jobs Explanation:New query-like programming extensions for MapReduce aim to improve performance for interactive jobs.
Q49.
What is the current limitation in terms of knowledge of MapReduce workloads?

a.

Lack of technology companies

b.

Limited scalability of MapReduce

c.

Limited understanding of query optimization

d.

Limited knowledge of real-life production workloads

Discuss
Answer: (d).Limited knowledge of real-life production workloads Explanation:Knowledge of real-life production MapReduce workloads is currently limited to a handful of technology companies.
Q50.
Which technologies are primarily associated with data analytics?

a.

Artificial intelligence and machine learning

b.

Relational DBMS, data warehousing, and ETL

c.

Business process management and optimization techniques

d.

Multivariate statistical analysis and web crawling

Discuss
Answer: (b).Relational DBMS, data warehousing, and ETL Explanation:Data analytics relies mostly on mature commercial technologies like relational DBMS, data warehousing, ETL, OLAP, and BPM.
Page 5 of 15

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