NPTEL Business Intelligence & Analytics Week 3 Assignment Answers 2024

Join Our WhatsApp Group Join Now
Join Us On Telegram Join Now

NPTEL Business Intelligence & Analytics Week 3 Assignment Answers 2024

1. Which database schema is typically associated with OLAP systems?

  • Entity-Relationship (ER) schema
  • Star or Snowflake schema
  • Relational schema
  • Object-oriented schema
Answer :- For Answer Click Here 

2. In a data cube, what are dimensions primarily representing?

  • Numeric measures
  • Facts related to sales
  • Entities or perspectives for record-keeping
  • Tables associated with facts
Answer :- For Answer Click Here 

3. What metaphor is used to describe multidimensional data storage in data warehousing?

  • Lattice
  • Apex cuboid
  • Data cube
  • Base cuboid
Answer :- For Answer Click Here 

4. What does the apex cuboid in a data cube typically represent?

  • Lowest level of summarization
  • Highest level of summarization
  • Total sales or dollars sold
  • Entities or perspectives for record-keeping
Answer :- 

5. How many cuboids are there in a 4-dimensional cube with 4 levels each?

  • 625 cuboids
  • 725 cuboids
  • 125 cuboids
  • 525 cuboids
Answer :- 

6. What is a significant difference between a snowflake schema and a star schema?

  • Higher redundancy in dimension tables
  • Increased efficiency in querying
  • Normalization of dimension tables
  • Dimension tables linked directly to the fact table
Answer :- For Answer Click Here 

7. Which schema is commonly used in data warehouses due to its capability to model multiple, interrelated subjects?

  • Star schema
  • Snowflake schema
  • Fact constellation
  • Entity-relationship model
Answer :- 

8. Which normal form deals with atomicity and ensures that each attribute contains only indivisible values?

  • First Normal Form (1NF)
  • Second Normal Form (2NF)
  • Third Normal Form (3NF)
  • Boyce-Codd Normal Form (BCNF)
Answer :- 

9. In a relational database, what is the purpose of a foreign key?

  • It uniquely identifies each record in a table
  • It maintains referential integrity between tables.
  • It ensures all attributes are atomic.
  • It acts as a substitute for the primary key.
Answer :- 

10. Consider the SQL statement: SELECT COUNT (*) FROM table_name. What does it retrieve?

  • All entries with * in the table
  • The number of unique values in the table
  • All rows in the table
  • The average value across all columns
Answer :- For Answer Click Here 

11. What is the primary objective of normalizing a database?

  • To eliminate data redundancy and minimize data inconsistency
  • To increase data duplication for faster retrieval
  • To combine tables for simplification
  • To allow for more complex queries
Answer :- 

12. Which normalization form ensures that every non-prime attribute is fully functionally dependent on the primary key, eliminating all transitive dependencies?

  • Second Normal Form (2NF)
  • Third Normal Form (3NF)
  • Boyce-Codd Normal Form (BCNF)
  • Fourth Normal Form (4NF)
Answer :- 

13. What is the purpose of generating a lattice of cuboids in a data cube model?

  • To display data at various levels of summarization based on different dimensions
  • To limit data visualization to a three-dimensional representation
  • To establish a relationship between the number of dimensions and the quantity of facts
  • To organize data in a hierarchical manner for easier access
Answer :- For Answer Click Here 

14. What distinguishes a data mart from a data warehouse in terms of schema preference?

  • Data marts prioritize the fact constellation schema, whereas data warehouses prefer snowflake schemas.
  • Data warehouses commonly employ star schema, while data marts usually opt for snowflake schemas
  • Data marts typically utilize star or snowflake schemas, while data warehouses Favor the fact constellation schema
  • Data warehouses exclusively use star schemas, whereas data marts solely rely on snowflake schemas.
Answer :- 

15. What characterizes the Roll-up operation in OLAP?

  • It aggregates data by stepping up a concept hierarchy or by adding dimensions.
  • It drills down into more detailed data by ascending a concept hierarchy.
  • It removes one or more dimensions from the cube, reducing its granularity.
  • It visualizes data by rotating the axes to provide an alternative presentation.
Answer :- For Answer Click Here