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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 9 of 15

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Q81.
What are the two crucial aspects that enable parallel execution in database systems?

a.

High-level language support and indexing options

b.

Transparent storage details and join strategies

c.

Query optimization and SQL commands

d.

Data partitioning and distributed storage

Discuss
Answer: (b).Transparent storage details and join strategies Explanation:The two crucial aspects that enable parallel execution in database systems are transparent storage details (e.g., indexing options and join strategies) and data partitioning.
Q82.
What does the optimizer in a parallel DBMS translate SQL commands into?

a.

High-level language

b.

Data partitioning strategy

c.

Query plan for parallel execution

d.

MapReduce tasks

Discuss
Answer: (c).Query plan for parallel execution Explanation:The optimizer in a parallel DBMS translates SQL commands into a query plan for parallel execution.
Q83.
In a parallel DBMS, when does the filter subquery in a SQL command get performed in parallel?

a.

During the Reduce phase

b.

In the Map function

c.

Before data loading

d.

After the join operation

Discuss
Answer: (b).In the Map function Explanation:The filter subquery in a SQL command is performed in parallel in a parallel DBMS, similar to the filtering performed in the Map function in a MapReduce system.
Q84.
What is the final step required in a parallel DBMS after performing a hash join and calculating preliminary aggregate functions?

a.

Data partitioning

b.

Query optimization

c.

Roll-up computation

d.

MapReduce task

Discuss
Answer: (c).Roll-up computation Explanation:In a parallel DBMS, the final step required after performing a hash join and calculating preliminary aggregate functions is a roll-up computation to produce the final answer.
Q85.
Why is there a need for interfaces between MapReduce systems and DBMSs?

a.

To replace DBMSs with MapReduce systems

b.

To enable MapReduce systems to perform filtering

c.

To make DBMSs more efficient

d.

To leverage the capabilities of both systems for complex analytical problems

Discuss
Answer: (d).To leverage the capabilities of both systems for complex analytical problems Explanation:The need for interfaces between MapReduce systems and DBMSs is to leverage the capabilities of both systems for complex analytical problems.
Q86.
What is the result of integrating MapReduce and DBMSs for complex analytical problems?

a.

Increased complexity

b.

Inefficiency

c.

Improved overall system efficiency

d.

Reduced data partitioning

Discuss
Answer: (c).Improved overall system efficiency Explanation:Integrating MapReduce and DBMSs for complex analytical problems results in improved overall system efficiency.
Q87.
What is the primary purpose of the MapReduce system in the context of interfacing with DBMSs?

a.

To replace the DBMS entirely

b.

To perform preliminary aggregations

c.

To perform filtering on large datasets

d.

To allow each system to leverage its strengths

Discuss
Answer: (d).To allow each system to leverage its strengths Explanation:The primary purpose of the MapReduce system in the context of interfacing with DBMSs is to allow each system to leverage its strengths.
Q88.
What is the primary focus of machine learning?

a.

Memorization by repetition

b.

Understanding human learning

c.

Constructing computer programs

d.

Identifying valid patterns in data

Discuss
Answer: (c).Constructing computer programs Explanation:Machine learning is primarily concerned with constructing computer programs that automatically improve with experience.
Q89.
Which type of learning is often used in knowledge discovery, data mining, and machine learning?

a.

Learning by being told

b.

Learning by analogy

c.

Learning from examples

d.

Rote learning

Discuss
Answer: (c).Learning from examples Explanation:Learning from examples, which involves inductive inference, is commonly used in knowledge discovery, data mining, machine learning, and related disciplines.
Q90.
What are the two main goals of the learning from data process?

a.

Verification of the user's hypothesis and description of patterns

b.

Discovery of new patterns and verification of data accuracy

c.

Prediction of the future and description of the past

d.

Classification and clustering of data

Discuss
Answer: (a).Verification of the user's hypothesis and description of patterns Explanation:The two main goals of the learning from data process are the verification of the user's hypothesis and the discovery of new patterns, which can further be divided into prediction and description.
Page 9 of 15

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