Data science combines mathematics, statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning to uncover actionable insights hidden in an organization’s data used to guide decision making and strategic planning
The post is about MCQs Big Data Questions with Answers. There are 20 multiple-choice questions with answers. “Ready to test your big data knowledge? Take a quiz today and see how you fare! Share your results in the comments and let us know what topics you’d like to see covered in future quizzes.” Let us start with the Online MCQs Big Data Questions now.
MCQs Big Data Questions with Answers
MCQs Big Data Questions
Which is the most compelling reason why mobile advertising is related to big data?
Which of the following summarizes the process of using data streams?
These two characteristics define the ratio between populated and unpopulated cells in a data source.
What does it mean for a device to be “smart”?
At-rest and in-transit data each have unique security concerns.
What does the term “in situ” mean in the context of big data?
What are data silos and why are they bad?
—————— is a measure of how fast the data is coming in.
These two characteristics are critical to implementing a successful high-velocity data strategy
What are the steps required for data analysis?
Which of the following is a technique mentioned in the videos for building a model?
Which of the Big Data processing tools provides distributed storage and processing of Big Data?
What does the attribute “Veracity” imply in the context of Big Data?
Defining the ————– ————– is the first step in any big data strategy.
A well-defined and comprehensive big data strategy makes the benefits of big data ————— for the organization.
What are the ways to address data quality issues?
Data in a data lake is most commonly stored in its natural or raw form.
What is the benefit of using pre-built Hadoop images?
Which of the following are general requirements for a programming language to support big data models?
Which of the following is the best description of why it is important to learn about the foundations of big data?
This post is about Data Mining Short Questions and Answers. The Data Mining Short Questions and Answers are related to Different levels of Analysis, Techniques used for Data Mining, Steps Used in Data Mining, Steps involved in Data Mining Knowledge Process, Data Aggregation, Data Generalization, and Book names related to Data Mining.
Table of Contents
Data Mining Short Questions and Answers
What is the History of Data Mining?
In the 1960s, statisticians used the terms Data Fishing or Data Dredging. Consequently, the term Data Mining appeared in 1990, especially in the database community.
Name Different Levels of Analysis of Data Mining
Artificial Neural Networks (ANNs)
Genetic Algorithms
Nearest Neighbour Method
Rule Induction
Data Visualization
What Techniques are Used for Data Mining?
The following techniques are used for data mining:
Artificial Neural Networks: Generally, data mining is used in many ways. Artificial Neural Networks (ANNs), a type of machine learning algorithm, are used in data mining to identify patterns, make predictions, and extract knowledge from large datasets, forming the basis of deep learning. It is also used for non-linear predictive models.
Decision Trees: Generally, tree-shaped structures are used to represent sets of decisions. It is also used for the classification of dataset rules are generated. A decision tree is a non-parametric supervised learning algorithm, utilized for both classification and regression tasks. It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes, and leaf nodes.
Genetic Algorithm: The genetic algorithms are present with the use of data mining as a powerful optimization technique to find the best solutions for complex problems, mimicking evolution to improve a population of potential solutions iteratively. Genetic algorithms are genetic combination, mutation, and natural selection for optimization techniques.
Name the Steps Used in Data Mining
Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment
Explain the Steps Involved in the Data Mining Knowledge Process
Data Cleaning: In the Data Cleaning Step, the noise and inconsistent data are removed.
Data Integration: In the Data Integration Step, multiple data sources are combined.
Data Selection: In the Data Selection Step, data relevant to the analysis tasks are retrieved from the data (or database).
Data Transformation: In the Data Transformation Step, data is transformed into different forms appropriate for data mining. The summary and aggregation operations are also performed in this step.
Data Mining: In the Data Mining Step, intelligent methods are applied to extract data patterns.
Pattern Evaluation: In The Pattern Evaluation Step, data patterns are evaluated.
Knowledge Presentation: In the Knowledge Presentation Step, knowledge is presented.
Name Some Data Mining Books
Introduction to Data Mining by Tan, Steinbach & Kumar (2006)
Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners
Data Science for Business: What you need to know about data mining and data analytic thinking
Probabilistic Programming and Bayesian Methods for Hackers
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: The Text Book by Charu C. Aggarwal (2015)
Data Mining: Practical Machine Learning Tools and Techniques by Ian Witten (2016)
Data Mining and Machine Learning: Fundamental Concepts and Algorithms by Mohammed J. Zaki, (2020)
What is Data Aggregation and Generalization?
Data Aggregation: Data aggregation is the process of combining and summarizing data from multiple sources into a single, more manageable format to facilitate analysis and decision-making
Generalization: It is a process where low-level data is replaced by high-level concepts so that the data can be generalized and meaningful. Generalization is often used to enhance privacy or summarize data for easier analysis, such as replacing specific dates with months or specific values with ranges.
The post is about the Big Data Quiz. There are 20 multiple-choice questions in this quiz. Are you Ready to test your big data knowledge? Take a quiz today and see how you fare! Share your results in the comments and let us know what topics you would like to see covered in future quizzes. Let us start with the Big Data Quiz.
Which one of the following is an example of structured data?
What is the reason behind the explosion of interest in big data?
Which of the following is an example of big data utilized in action today?
What reasoning was given for the following: why is the “data storage to price ratio” relevant to big data?
What is the best description of personalized marketing enabled by big data?
Of the following, which are some examples of personalized marketing related to big data?
What is the workflow for working with big data?
Big data best practice is to —————- whenever possible.
Which of the following are common big data strategies?
This many bytes of data are created daily.
Which of the following are types of data found in a big data environment?
Of the three data sources, which is the hardest to implement and streamline into a model
Where does the real value of big data often come from?
A big data strategy MUST be seen as something separate from the organizational strategy and kept separate at all costs.
Big data technologies can be largely classified into the following two groups?
What are the three types of diverse data sources?
What is an example of machine data?
What is an example of organizational data?
When dealing with high-velocity data, precautions, and processes should be implemented to investigate and analyze anomalies and other patterns of behavior.
