BI, Analytics, Data Science & AI Review
200 Original Practice Questions and Answers
Original review questions covering core business intelligence, analytics, data science, and AI topics.
FOUNDATIONS OF BI, ANALYTICS & AI
1. What is business intelligence (BI), broadly defined?
Answer: A set of technologies, applications, and processes for collecting, storing, analyzing, and providing access to data to
help enterprise users make better business decisions.
2. What is the general relationship between data, information, and knowledge in the context of analytics?
Answer: Data are raw facts; information is data processed into a meaningful context; knowledge is information combined
with experience/judgment used to make decisions.
3. What are the three main categories of analytics, from simplest to most advanced?
Answer: Descriptive analytics, predictive analytics, and prescriptive analytics.
4. What is descriptive analytics used for?
Answer: Summarizing historical data to understand what has happened, often through reporting and visualization.
5. What is predictive analytics used for?
Answer: Using historical data and statistical/machine learning techniques to forecast future outcomes or trends.
6. What is prescriptive analytics used for?
Answer: Recommending specific actions or decisions by combining predictive models with optimization and simulation
techniques.
7. What is the difference between structured and unstructured data?
Answer: Structured data is organized in a predefined format (e.g., rows/columns in a database); unstructured data lacks a
predefined format (e.g., text, images, video).
8. What is a decision support system (DSS)?
Answer: A computer-based information system that supports business or organizational decision-making activities, typically
for semi-structured or unstructured problems.
9. What is the general goal of using analytics and AI in organizational decision-making?
Answer: To improve the speed, accuracy, and quality of decisions by leveraging data-driven insights rather than relying solely
on intuition.
10. What is the difference between artificial intelligence (AI) and traditional rule-based programming?
Answer: AI systems can learn patterns from data and adapt behavior, whereas traditional programming follows explicitly
coded, fixed rules for every scenario.
DATA WAREHOUSING & DATA MANAGEMENT
11. What is a data warehouse?
Answer: A subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management
decision-making.
12. What is the difference between a data warehouse and an operational (transactional) database?
Answer: A data warehouse is optimized for complex analytical queries and historical reporting, while operational databases
are optimized for fast, real-time transaction processing.
13. What does the ETL process stand for, and what does it involve?
Answer: Extract, Transform, Load — extracting data from source systems, transforming it into a consistent format, and
loading it into the data warehouse.
14. What is a data mart?
, Answer: A smaller, more focused subset of a data warehouse, typically designed to serve the needs of a specific business unit
or department.
15. What is a star schema in data warehouse design?
Answer: A data modeling approach with a central fact table connected to multiple dimension tables, optimized for query
performance in analytical processing.
16. What is the difference between a fact table and a dimension table in a star schema?
Answer: A fact table contains quantitative, measurable data (e.g., sales amount); dimension tables contain descriptive
attributes (e.g., product, time, location) used to analyze the facts.
17. What is online analytical processing (OLAP)?
Answer: A category of software tools that enable users to interactively analyze multidimensional data from multiple
perspectives.
18. What is the difference between OLAP and OLTP (online transaction processing)?
Answer: OLAP supports complex analytical queries on historical, aggregated data; OLTP supports fast, day-to-day
transactional operations on current data.
19. What is data governance, and why is it important in enterprise analytics?
Answer: The overall management of data availability, usability, integrity, and security within an organization; important for
ensuring reliable, trustworthy analytics.
20. What is master data management (MDM)?
Answer: A discipline for creating and maintaining a single, consistent, accurate view of key business entities (e.g., customers,
products) across an organization's systems.
DATA MINING CONCEPTS & PROCESS
21. What is data mining?
Answer: The process of discovering useful patterns, relationships, and insights from large volumes of data using statistical and
computational techniques.
22. What are the two general categories of data mining tasks?
Answer: Predictive tasks (e.g., classification, regression) and descriptive tasks (e.g., clustering, association rule mining).
23. What is the CRISP-DM process model, and what does it stand for?
Answer: Cross-Industry Standard Process for Data Mining — a widely used framework outlining the stages of a data mining
project: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
24. What is classification in data mining?
Answer: A predictive technique that assigns data records to predefined categories or classes based on input features.
25. What is clustering in data mining?
Answer: An unsupervised technique that groups similar data points together based on shared characteristics, without
predefined class labels.
26. What is association rule mining, and give a classic business example.
Answer: A technique for discovering relationships between variables in large datasets; a classic example is market basket
analysis, identifying products frequently purchased together.
27. What is the difference between supervised and unsupervised learning in data mining/machine learning?
Answer: Supervised learning uses labeled data with known outcomes to train a model; unsupervised learning finds patterns
in data without predefined labels or outcomes.
28. What is overfitting in a predictive model, and why is it a concern?
Answer: When a model learns the training data too closely, including noise, resulting in poor generalization/performance on
new, unseen data.
29. What is the purpose of splitting data into training and testing (holdout) sets in a data mining project?
Answer: To evaluate how well a model generalizes to new data rather than only assessing performance on the data it was
trained on.
200 Original Practice Questions and Answers
Original review questions covering core business intelligence, analytics, data science, and AI topics.
FOUNDATIONS OF BI, ANALYTICS & AI
1. What is business intelligence (BI), broadly defined?
Answer: A set of technologies, applications, and processes for collecting, storing, analyzing, and providing access to data to
help enterprise users make better business decisions.
2. What is the general relationship between data, information, and knowledge in the context of analytics?
Answer: Data are raw facts; information is data processed into a meaningful context; knowledge is information combined
with experience/judgment used to make decisions.
3. What are the three main categories of analytics, from simplest to most advanced?
Answer: Descriptive analytics, predictive analytics, and prescriptive analytics.
4. What is descriptive analytics used for?
Answer: Summarizing historical data to understand what has happened, often through reporting and visualization.
5. What is predictive analytics used for?
Answer: Using historical data and statistical/machine learning techniques to forecast future outcomes or trends.
6. What is prescriptive analytics used for?
Answer: Recommending specific actions or decisions by combining predictive models with optimization and simulation
techniques.
7. What is the difference between structured and unstructured data?
Answer: Structured data is organized in a predefined format (e.g., rows/columns in a database); unstructured data lacks a
predefined format (e.g., text, images, video).
8. What is a decision support system (DSS)?
Answer: A computer-based information system that supports business or organizational decision-making activities, typically
for semi-structured or unstructured problems.
9. What is the general goal of using analytics and AI in organizational decision-making?
Answer: To improve the speed, accuracy, and quality of decisions by leveraging data-driven insights rather than relying solely
on intuition.
10. What is the difference between artificial intelligence (AI) and traditional rule-based programming?
Answer: AI systems can learn patterns from data and adapt behavior, whereas traditional programming follows explicitly
coded, fixed rules for every scenario.
DATA WAREHOUSING & DATA MANAGEMENT
11. What is a data warehouse?
Answer: A subject-oriented, integrated, time-variant, and non-volatile collection of data used to support management
decision-making.
12. What is the difference between a data warehouse and an operational (transactional) database?
Answer: A data warehouse is optimized for complex analytical queries and historical reporting, while operational databases
are optimized for fast, real-time transaction processing.
13. What does the ETL process stand for, and what does it involve?
Answer: Extract, Transform, Load — extracting data from source systems, transforming it into a consistent format, and
loading it into the data warehouse.
14. What is a data mart?
, Answer: A smaller, more focused subset of a data warehouse, typically designed to serve the needs of a specific business unit
or department.
15. What is a star schema in data warehouse design?
Answer: A data modeling approach with a central fact table connected to multiple dimension tables, optimized for query
performance in analytical processing.
16. What is the difference between a fact table and a dimension table in a star schema?
Answer: A fact table contains quantitative, measurable data (e.g., sales amount); dimension tables contain descriptive
attributes (e.g., product, time, location) used to analyze the facts.
17. What is online analytical processing (OLAP)?
Answer: A category of software tools that enable users to interactively analyze multidimensional data from multiple
perspectives.
18. What is the difference between OLAP and OLTP (online transaction processing)?
Answer: OLAP supports complex analytical queries on historical, aggregated data; OLTP supports fast, day-to-day
transactional operations on current data.
19. What is data governance, and why is it important in enterprise analytics?
Answer: The overall management of data availability, usability, integrity, and security within an organization; important for
ensuring reliable, trustworthy analytics.
20. What is master data management (MDM)?
Answer: A discipline for creating and maintaining a single, consistent, accurate view of key business entities (e.g., customers,
products) across an organization's systems.
DATA MINING CONCEPTS & PROCESS
21. What is data mining?
Answer: The process of discovering useful patterns, relationships, and insights from large volumes of data using statistical and
computational techniques.
22. What are the two general categories of data mining tasks?
Answer: Predictive tasks (e.g., classification, regression) and descriptive tasks (e.g., clustering, association rule mining).
23. What is the CRISP-DM process model, and what does it stand for?
Answer: Cross-Industry Standard Process for Data Mining — a widely used framework outlining the stages of a data mining
project: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
24. What is classification in data mining?
Answer: A predictive technique that assigns data records to predefined categories or classes based on input features.
25. What is clustering in data mining?
Answer: An unsupervised technique that groups similar data points together based on shared characteristics, without
predefined class labels.
26. What is association rule mining, and give a classic business example.
Answer: A technique for discovering relationships between variables in large datasets; a classic example is market basket
analysis, identifying products frequently purchased together.
27. What is the difference between supervised and unsupervised learning in data mining/machine learning?
Answer: Supervised learning uses labeled data with known outcomes to train a model; unsupervised learning finds patterns
in data without predefined labels or outcomes.
28. What is overfitting in a predictive model, and why is it a concern?
Answer: When a model learns the training data too closely, including noise, resulting in poor generalization/performance on
new, unseen data.
29. What is the purpose of splitting data into training and testing (holdout) sets in a data mining project?
Answer: To evaluate how well a model generalizes to new data rather than only assessing performance on the data it was
trained on.
