TRUE

FALSE

TRUE

FALSE

Structured Data

Unstructured Data

Semi-structured Data

Raw Data

Process structured data by sorting, grouping, summing, filtering, and formatting

Use sophisticated statistical techniques

Volume, velocity, and variety force use of MapReduce techniques

Printed (static) and dynamic reports

To use a prior model to compute the outcome

To start with a prior hypothesis or model

To understand (explore) data

To predict using multiple linear regression

A programming language used for web development.

A type of computer virus.

A method for organizing files on a computer.

A computational model inspired by the structure and function of biological neural networks in the brain.

Supervised learning is a type of machine learning where the model is trained using unlabeled data.

Supervised learning is a type of machine learning where the model does not require any training.

Supervised learning is a type of machine learning where the model is trained using reinforcement learning techniques.

Supervised learning is a type of machine learning where the model is trained using labeled data.

contact list

product databases

customer's feedback

customer's information

By randomly selecting items to recommend.

By analyzing market trends to make recommendations.

By using a rule-based system to generate recommendations.

By analyzing user preferences and behavior to make personalized recommendations.

data dredging

knowledge mining from data

knowledge extraction

data/pattern analysis

data archaeology

1. Warehousing

1. Data Mining

1. Text Mining

1. Data Selection

True

False

Unsupervised Learning

Supervised learning

Semi supervised learning

None of these

Classification

Clustering

Regression

Linear Programming

Data visualization, data analysis, data interpretation

Data collection, preprocessing, text analysis, and interpretation of results

Text processing, text summarization, text categorization

Data extraction, data storage, data retrieval

It has no impact on the accuracy of the analysis

It only works for certain types of text data

It slows down the text mining process

It helps to clean and prepare the text data for analysis.

A. collection of tables

B. set of software programs

c. Both A and B

None

multidimensional schema

data cubes

data cells

all of the above

Unsupervised learning is a type of machine learning where the model requires a large amount of labeled data to train effectively.

Unsupervised learning is a type of machine learning where the model learns from labeled examples and guidance from a supervisor.

Unsupervised learning is a type of machine learning where the model learns patterns and relationships in the data without any labeled examples or guidance from a supervisor.

Unsupervised learning is a type of machine learning where the model only learns patterns and relationships in the data without making any predictions.

Volume

Variety

Vision

Velocity

Text mining is only used for analyzing written text, while natural language processing can also analyze spoken language.

Text mining focuses on grammar and syntax, while natural language processing focuses on semantics and meaning.

Text mining involves extracting useful information from unstructured text, while natural language processing focuses on the interaction between computers and human language.

Text mining is used for analyzing structured data, while natural language processing is used for unstructured data.

huge amount of space

large, diverse sets of information

unlimited speed data internet connection

small, undiverse sets of information

Identifying customer preferences

Analyzing emotional tone in text

Monitoring brand perception

Understanding public policy decisions

Sentiment analysis, topic modeling, document clustering, word frequency analysis

Tokenization, stemming, lemmatization, named entity recognition

Spelling correction, punctuation removal, synonym replacement, grammar checking

Alphabetization, categorization, summarization, keyword extraction

None of the above

Capturing overall sentiment in a larger piece of writing

Identifying areas for improvement

Understanding customer feedback

Artificial neural networks analyze data and identify patterns and anomalies to detect fraudulent activity.

Artificial neural networks use machine learning algorithms to detect fraudulent activity.

Artificial neural networks rely on human input to identify patterns and anomalies in data.

Artificial neural networks are not effective in fraud detection and are rarely used.

The text does not express any positive or negative sentiment or Emotional Sentiment

It can be used for classification

it can be used for regression

It can be used in both classification and regression

To decide whether a text expresses positive, negative or neutral sentiment.

To determine the overall accuracy of the techniques used for sentiment analysis.

To remove emoticons and URLs

Operational efficiency tracking

Focus on customer satisfaction

Employee engagement metrics

Measure financial performance and outcomes

Loading data into the data warehouse

Extracting data from various sources

Transforming the extracted data

Analyzing the data for insights

Return on Investment (ROI), Revenue Growth, Cost Reduction, Cash Flow

Net Profit Margin, Employee Satisfaction, Market Share

Gross Margin, Earnings per Share, Return on Equity

Customer Retention Rate, Return on Assets, Inventory Turnover

Customer feedback process in a service industry

Marketing campaign strategy in a software company

Order fulfillment process in a manufacturing company

Employee break schedule in a retail store

Extract, Transform, Load

Execute, Transfer, Load

Extract, Translate, Link

Encode, Transform, Log

The number of neighbors to consider for classification

The number of features in the dataset

The number of classes in the dataset

The distance metric used in the algorithm

Considerations related to privacy, consent, data security, and potential biases

Not obtaining consent for data collection

Ignoring potential biases

Using only unstructured data

Data is loaded into a target database.

Data is extracted from source systems.

Data is cleaned, validated, and reformatted.

Data is analyzed for business insights.

The practice of turning text into physical objects.

The process of deriving high-quality information from text.

The act of physically removing words from a page.

The process of extracting minerals from written documents.

It has no significant impact on BI.

It complicates the BI process by adding more data.

It ensures the data used for BI is clean, consistent, and comprehensive.

It slows down the BI process by adding extra steps.

A hyperplane in SVM is a decision boundary that separates classes in a dataset based on their features.

A hyperplane in SVM is a type of airplane used for data analysis

A hyperplane in SVM is a mathematical equation used to calculate probabilities

A hyperplane in SVM is a type of software used for image processing

is a personal choice that shouldn't be discussed in public

depends on why you are clustering the data

should always be as large as your computer system can handle

has maximum 10

It helps in saving data locally.

It assists in real-time data analysis only.

It enables data consolidation from multiple sources for analysis and decision-making.

It replaces the need for databases.

By measuring the number of employees trained in customer service

By monitoring the CEO's salary

By tracking the company's revenue growth

By tracking metrics such as customer satisfaction scores, customer retention rates, customer lifetime value, net promoter score, and number of customer complaints resolved.

Minimize the distance between data points

Maximize the margin between the decision boundary and the nearest data points

Minimize the number of support vectors

Maximize the number of support vectors

Supervised learning

Unsupervised learning

Reinforcement learning

Semi-supervised learning