Business Intelligence & Analytics Week 1 NPTEL Assignment Answers 2026

1. Which step is defined as the extraction of non-obvious patterns from large datasets using intelligent methods?

• Knowledge Discovery
• Data integration
• Data selection
• Data mining

Answer : See Answers 

2. According to the lecture, Google operates as a “two-sided market” facilitated by matchmaking algorithms. Which of the following pairs accurately identifies the two distinct groups that the platform connects to generate revenue?

• Information Seekers and Content Publishers.
• Search Engine Users and Advertisers.
• Data Scientists and Algorithm Developers.
• Digital Service Subscribers and Cloud Storage Providers.

Answer :

3. Which of the following is an example of unstructured data in a hospital information system?

• Doctor’s handwritten or typed clinical notes
• Patient admission records stored in database tables
• Laboratory test results recorded in numeric form
• Standardized patient demographics forms

Answer :

4. Which of the following best describes the core objective of data mining?

• Efficiently storing and archiving massive datasets
• Creating social networks and communities
• Converting raw data into organized, useful knowledge.
• Supporting scientific experiments and observations

Answer :

5. Automatically organizing books into predefined categories in a library system is an example of _________.

• clustering
• unsupervised learning
• supervised learning
• reinforcement learning

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6. True or False: Analyzing past airline travel records to report that ‘trips to the top 30 tourist destinations dropped by 10%’ is an example of descriptive data mining.

Answer :

7. __________ refers to data objects that deviate from the general behaviour or model of the dataset and can be especially important in applications such as fraud detection.

• Clusters
• Outliers
• Attributes
• Outbounds

Answer :

8. Which statement correctly describes a key function of a data warehouse?

• It merges data from multiple sources and structures it into multidimensional data cubes.
• It stores only real-time transactional updates for immediate processing.
• It replaces indexing methods by eliminating the need for optimized query processing.
• It restricts data to a single time period to ensure consistency.

Answer :

9. In business intelligence, clustering is mainly used:

• To create data warehouses
• To enhance data visualization
• To store historical data in standardized formats
• To group customers based on similar behaviours or attributes

Answer :

10. A key structural feature of a relational database is that it is organized as __.

• a set of interconnected graphs
• a collection of tables with unique names
• a repository mainly for multimedia files
• a network of entities linked through pointers

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11. The long tail phenomenon describes how businesses earn revenue from:

• The cumulative sales of many niche items that generate significant revenue
• The cumulative sales of popular items that generate significant revenue
• Minimizing inventory costs to maximize profit
• Offering product diversity based on market demand

Answer :

12. What is the correct sequence of steps in data preparation for Knowledge Discovery from Data (KDD)?

• Data cleaning, data integration, data transformation, data selection
• Data cleaning, data integration, data visualization, data selection
• Data cleaning, data standardization, data transformation, data selection
• Data cleaning, data standardization, data normalization, data selection

Answer :

13. The architecture of a data warehouse is primarily designed to support ____________.

• routine data cleaning tasks and integration
• advanced query optimization
• high-level management decision making
• real-time transaction data processing

Answer :

14. A retail chain stores customer transactions, loyalty card information, and E-commerce records in separate systems. Before generating a unified customer profile and analyzing their overall buying behaviour, the company must perform ____________ to combine these different data sources into a single, consistent dataset.

• Data cleaning
• Data integration
• Data transformation
• Data reduction

Answer :

15. According to the lecture, which of the following terms belongs to the “vocabulary of analytics”?

• OLAP
• Data warehousing
• ETL and DSS
• All the options given

Answer : See Answers 

NPTEL Business Intelligence & Analytics Week 1 Assignment Answers 2024

Q1. What concept does the phrase “turning data tombs into ‘golden nuggets’ of knowledge” signify with respect to data mining?

a) The transformation of extensive data reserves into valuable insights and knowledge.
b) The replacement of conventional data repositories with intuitive decision-making tools.
c) The extraction of specific data sets for expert systems’ utilization.
d) The integration of data archives with cutting-edge data mining technologies.

Answer: a
Explanation:
This metaphor highlights the value extraction from vast, unprocessed data (the “tombs”) to derive actionable knowledge (the “nuggets”).

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Q2. Which step involves the extraction of data patterns using intelligent methods?

a) Data cleaning
b) Data integration
c) Data selection
d) Data mining

Answer: d
Explanation:
Data mining is the process of discovering patterns, correlations, trends, and useful information from large data sets using statistical, machine learning, or AI methods.

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Q3. What is the primary purpose of data mining in the context of the data age?

a) Storing and organizing massive amounts of data
b) Creating social networks and communities
c) Uncovering valuable information from vast data and converting it into organized knowledge
d) Supporting scientific experiments and observations

Answer: c
Explanation:
Data mining aims to extract meaningful patterns from big data, aiding in decision-making and strategic planning.

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Q4. Which technology contributed substantially to the evolution and wide acceptance of relational technology for efficient storage, retrieval, and management of large amounts of data?

a) Advanced indexing and accessing methods
b) Online transaction processing (OLTP)
c) Hierarchical database systems
d) Object-oriented database models

Answer: a
Explanation:
Advanced indexing and data access methods greatly improved performance and scalability, making relational databases efficient and widely used.

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Q5. What does the architecture of a data warehouse primarily aim to facilitate?

a) Data cleaning and integration
b) Advanced query optimization
c) Management decision making
d) Real-time data processing

Answer: c
Explanation:
A data warehouse is designed to help with strategic decisions by providing integrated, historical, and summarized data to managers.

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Q6. What is the primary advantage of using data warehouse systems for OLAP?

a) Providing detailed transactional views to users
b) Performing daily transaction analysis
c) Presenting data at different levels of abstraction
d) Enabling real-time data updates and modifications

Answer: c
Explanation:
OLAP (Online Analytical Processing) enables users to analyze data hierarchically, e.g., monthly, quarterly, or yearly reports.

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Q7. What defines outliers in a dataset?

a) Objects that conform to the general behaviour or model of the data.
b) Data objects that are commonly discarded as exceptions.
c) Data that adhere strictly to statistical or distance measures.
d) Objects that deviate from the general behaviour or model of the data.

Answer: d
Explanation:
Outliers are data points that do not fit the expected pattern or distribution and may indicate anomalies or errors.

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Q8. How does data mining benefit from scalable database technologies?

a) It diminishes efficiency and scalability on large datasets
b) It relies solely on traditional database systems
c) It enables high efficiency and scalability on large datasets
d) It limits scalability to small datasets for efficiency

Answer: c
Explanation:
Scalable technologies allow data mining to handle big data efficiently, which is critical in modern applications.

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Q9. What distinguishes Descriptive Analytics from other types of analytics?

a) It focuses on predicting future outcomes.
b) It identifies patterns and trends from past data.
c) It prescribes actions based on analyzed data.
d) It evaluates data in real-time for immediate decisions.

Answer: b
Explanation:
Descriptive analytics answers “what happened” by summarizing past data to identify trends and patterns.

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Q10. Which phase in the knowledge discovery process involves the removal of noise and inconsistent data?

a) Data integration
b) Data transformation
c) Data cleaning
d) Data selection

Answer: c
Explanation:
Data cleaning ensures quality by removing incorrect, inconsistent, or noisy data, making it suitable for analysis.

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Q11. In the context of data preprocessing, what is the purpose of data transformation?

a) To retrieve relevant data from the database
b) To remove noise and inconsistent data
c) To prepare data for mining by performing summary or aggregation operations
d) To evaluate interesting patterns based on specific measures

Answer: c
Explanation:
Data transformation includes normalization, summarization, or aggregation to make the data suitable for mining algorithms.

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Q12. What is the fundamental characteristic of a relational database?

a) It consists of a collection of tables with unique names.
b) It uses graphs or networked structures to store data.
c) It primarily stores multimedia data and text data.
d) It represents the database as a set of entities and their relationships.

Answer: a
Explanation:
Relational databases are structured as tables (relations), each with unique names, storing rows and columns of data.

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Q13. Which of the following can be called as a major driver of Data Mining?

a) Decline of open-source technologies
b) Declining growth in Manufacturing sector globally
c) Penetration of MOOC platforms
d) Rise of transaction processing systems/ERPs

Answer: d
Explanation:
The growth of ERP and transactional systems has led to massive data generation, creating a need for data mining tools.

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Q14. What does the “long tail” phenomenon refer to in business?

a) A marketing strategy focused on a narrow customer base
b) The distribution of sales that extends to less common products returning substantial profits
c) A strategy to increase product diversity without considering market demand
d) A method to minimize inventory costs in retail businesses

Answer: b
Explanation:
The long tail shows that niche products can collectively generate more revenue than a few mainstream bestsellers.

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Q15. What can you infer from the following graph?

a) Less travelled destinations are growing more popular with each passing year
b) Top travel destinations are becoming more popular with each passing year
c) The growth of ICT evidently played an important role in making least popular places more popular
d) The growth of social media evidently played an important role in making least popular places more popular

Answer: d
Explanation:
The graph likely shows how social media influence makes lesser-known destinations more visible and attractive, shifting tourism trends.