HomeSubjectsUniversityBlogAbout

Data Warehousing & Data Mining

Topic in Databases

210 total MCQsShowing 30 with explanations10 Easy10 Medium10 Hard
Start Timed Quiz on Data Warehousing & Data MiningBack to Databases

About This Topic

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data that supports analysis and decision-making. Modelling questions cover star and snowflake schemas, fact tables and their measures, dimension tables, degenerate dimensions, transaction versus periodic snapshot fact tables, and slowly changing dimensions of types 1, 2 and 3. You should compare OLTP with OLAP, describe roll-up, drill-down, slice, dice and pivot operations, and place ETL, data marts and materialized views in the architecture. The data mining part tests association rules with support and confidence, the Apriori algorithm, classification, and clustering methods such as k-means.

Below are 30 practice questions from a pool of 210 Data Warehousing & Data Mining MCQs, one of 17 topics in Databases. Each shows the correct answer with an explanation; when you are ready, take a timed quiz to test recall under exam conditions.

Practice Questions

Each question below shows the correct answer with a full explanation. Use these to build conceptual understanding before attempting a timed quiz.

Data Warehousing & Data MiningEasy

Q1. A data warehouse is:

  1. A.A transactional database designed for processing day-to-day operations in real-time mode
  2. B.A subject-oriented, integrated, time-variant, non-volatile collection of data for decisions✓ Correct
  3. C.A backup storage mechanism that periodically copies data for disaster recovery plans
  4. D.A type of NoSQL database that supports flexible schemas and horizontal scaling systems

Explanation

A data warehouse is designed for analysis and reporting, storing historical, integrated data.

Report an error in this question

Data Warehousing & Data MiningEasy

Q2. OLAP stands for:

  1. A.Offline Analytical Processing
  2. B.Online Application Programming
  3. C.Online Automated Processing
  4. D.Online Analytical Processing✓ Correct

Explanation

OLAP stands for Online Analytical Processing, used for complex analysis of data.

Report an error in this question

Data Warehousing & Data MiningEasy

Q3. OLTP stands for:

  1. A.Online Text Processing
  2. B.Online Transaction Processing✓ Correct
  3. C.Online Table Processing
  4. D.Offline Transaction Processing

Explanation

OLTP stands for Online Transaction Processing, used for day-to-day transactional operations.

Report an error in this question

Data Warehousing & Data MiningEasy

Q4. The ETL process stands for:

  1. A.Export, Translate, Link
  2. B.Enter, Transfer, List
  3. C.Extract, Transfer, Log
  4. D.Extract, Transform, Load✓ Correct

Explanation

ETL stands for Extract (from sources), Transform (clean and format), Load (into the warehouse).

Report an error in this question

Data Warehousing & Data MiningEasy

Q5. A fact table in a data warehouse contains:

  1. A.Quantitative measures and foreign keys to dimension tables✓ Correct
  2. B.Only dimension attributes used for filtering and grouping
  3. C.Only text descriptions without any numeric measure values
  4. D.Only primary keys without any quantitative measure data

Explanation

A fact table stores quantitative data (measures) and keys linking to dimension tables.

Report an error in this question

Data Warehousing & Data MiningEasy

Q6. A dimension table in a data warehouse contains:

  1. A.Descriptive attributes used for filtering and grouping✓ Correct
  2. B.Log entries recording all data warehouse load operations
  3. C.Transaction records from the operational source systems
  4. D.Quantitative measures like sales amounts and quantities

Explanation

Dimension tables store descriptive attributes (e.g., product name, date, location) for analysis context.

Report an error in this question

Data Warehousing & Data MiningEasy

Q7. The star schema consists of:

  1. A.Multiple fact tables connected to each other in it
  2. B.A central fact table connected to dimension tables✓ Correct
  3. C.Only dimension tables without any fact table data
  4. D.Only fact tables without any dimension table data

Explanation

A star schema has a central fact table surrounded by dimension tables in a star-like pattern.

Report an error in this question

Data Warehousing & Data MiningEasy

Q8. Data mining is:

  1. A.Writing SQL queries for retrieving records from relational tables
  2. B.Creating database backups for disaster recovery and data safety
  3. C.The process of discovering patterns and insights from large datasets✓ Correct
  4. D.Storing data in a warehouse for long-term historical analysis use

Explanation

Data mining uses algorithms to discover patterns, correlations, and insights in large datasets.

Report an error in this question

Data Warehousing & Data MiningEasy

Q9. A data mart is:

  1. A.A type of OLTP system designed for transactional data processing
  2. B.A subset of a data warehouse focused on a specific business area✓ Correct
  3. C.A complete data warehouse containing all enterprise data sources
  4. D.A NoSQL database designed for handling unstructured data at scale

Explanation

A data mart is a focused subset of a data warehouse serving a specific department or business function.

Report an error in this question

Data Warehousing & Data MiningEasy

Q10. Which of the following is an OLAP operation?

  1. A.INSERT
  2. B.Drill-down✓ Correct
  3. C.UPDATE
  4. D.DELETE

Explanation

Drill-down is an OLAP operation that navigates from summarized data to more detailed data.

Report an error in this question

Data Warehousing & Data MiningMedium

Q11. The snowflake schema extends the star schema by:

  1. A.Removing dimension tables from the schema completely
  2. B.Adding more fact tables to handle additional measures
  3. C.Normalizing dimension tables into sub-dimension tables✓ Correct
  4. D.Denormalizing all tables into one single flat structure

Explanation

The snowflake schema normalizes dimension tables, creating sub-dimension tables branching from dimensions.

Report an error in this question

Data Warehousing & Data MiningMedium

Q12. Roll-up in OLAP means:

  1. A.Drilling down to see more detailed level of data
  2. B.Selecting a specific dimension value for filtering
  3. C.Aggregating data to a higher level of summarization✓ Correct
  4. D.Rotating the data cube axes for a different view

Explanation

Roll-up aggregates data by climbing up a concept hierarchy (e.g., from city to country).

Report an error in this question

Data Warehousing & Data MiningMedium

Q13. Drill-down in OLAP means:

  1. A.Rotating the data cube to view a different perspective
  2. B.Navigating from summarized data to more detailed data✓ Correct
  3. C.Aggregating data to higher levels of summarization
  4. D.Removing dimensions from the cube for simplification

Explanation

Drill-down moves from summary data to more detailed levels (e.g., from year to quarter to month).

Report an error in this question

Data Warehousing & Data MiningMedium

Q14. Slice operation in OLAP:

  1. A.Selects a single value for one dimension, creating a sub-cube✓ Correct
  2. B.Aggregates all dimensions into one single summary result
  3. C.Selects multiple values for multiple dimensions simultaneously
  4. D.Rotates the cube to provide an alternative data perspective

Explanation

Slice creates a sub-cube by fixing one dimension at a particular value.

Report an error in this question

Data Warehousing & Data MiningMedium

Q15. Dice operation in OLAP:

  1. A.Selects a single dimension value for data filtering
  2. B.Rotates the cube axes for an alternative data view
  3. C.Selects specific values for two or more dimensions✓ Correct
  4. D.Aggregates data across all available dimension types

Explanation

Dice selects a sub-cube by specifying criteria on two or more dimensions.

Report an error in this question

Data Warehousing & Data MiningMedium

Q16. Pivot (rotate) in OLAP:

  1. A.Deletes a dimension from the cube structure entirely
  2. B.Rotates the data axes to provide an alternative view✓ Correct
  3. C.Merges cubes from different source data warehouses
  4. D.Adds new data records into the existing data cube

Explanation

Pivot rotates the data cube to view data from different perspectives.

Report an error in this question

Data Warehousing & Data MiningMedium

Q17. MOLAP (Multidimensional OLAP) stores data in:

  1. A.A multidimensional array (cube) structure✓ Correct
  2. B.Key-value stores for fast data retrieval
  3. C.Flat files stored on the file system disk
  4. D.Relational tables with rows and columns

Explanation

MOLAP pre-computes and stores data in multidimensional arrays for fast query response.

Report an error in this question

Data Warehousing & Data MiningMedium

Q18. ROLAP (Relational OLAP) stores data in:

  1. A.Relational database tables✓ Correct
  2. B.Flat files on disk storage
  3. C.NoSQL database documents
  4. D.Multidimensional arrays

Explanation

ROLAP stores warehouse data in relational tables and uses SQL for OLAP operations.

Report an error in this question

Data Warehousing & Data MiningMedium

Q19. Association rule mining discovers:

  1. A.Database schemas and their normalization structure in the system
  2. B.Index structures and their configuration for faster data access
  3. C.Relationships between items (e.g., items frequently bought together)✓ Correct
  4. D.SQL queries and their execution plans for optimized performance

Explanation

Association rule mining finds interesting relationships (associations) between variables in large datasets.

Report an error in this question

Data Warehousing & Data MiningMedium

Q20. The Apriori algorithm is used in data mining for:

  1. A.Finding frequent itemsets and association rules✓ Correct
  2. B.Classification of records into known classes
  3. C.Regression analysis on continuous value data
  4. D.Clustering data into groups of similar items

Explanation

The Apriori algorithm identifies frequent itemsets and generates association rules from transactional data.

Report an error in this question

Data Warehousing & Data MiningHard

Q21. A slowly changing dimension (SCD) Type 2 handles changes by:

  1. A.Overwriting the old value directly in place without keeping any history of changes
  2. B.Adding a new row with the new value and preserving the old row with history tracking✓ Correct
  3. C.Deleting the old row entirely and inserting a brand new row with the changed value
  4. D.Adding a new column to store the updated value alongside the previous column value

Explanation

SCD Type 2 tracks history by creating a new row for each change, preserving the complete history.

Report an error in this question

Data Warehousing & Data MiningHard

Q22. A degenerate dimension is:

  1. A.A dimension key in the fact table that has no corresponding dimension table✓ Correct
  2. B.A dimension that is always empty and contains no rows of data whatsoever
  3. C.A dimension table with many attributes and a large number of data records
  4. D.A normalized dimension table that has been split into multiple sub-tables

Explanation

A degenerate dimension is a dimension key (like invoice number) stored in the fact table without a separate dimension table.

Report an error in this question

Data Warehousing & Data MiningHard

Q23. The galaxy schema (fact constellation) contains:

  1. A.No dimension tables in the schema at all
  2. B.Only dimension tables without fact tables
  3. C.Only one fact table with no shared dimensions
  4. D.Multiple fact tables sharing dimension tables✓ Correct

Explanation

A galaxy schema has multiple fact tables that can share common dimension tables.

Report an error in this question

Data Warehousing & Data MiningHard

Q24. Clustering in data mining groups:

  1. A.Data by primary key values in ascending sort order
  2. B.Data based on known categories and predefined classes
  3. C.Similar data points together without predefined labels✓ Correct
  4. D.Data randomly without any similarity considerations

Explanation

Clustering is unsupervised learning that groups similar data points without predefined class labels.

Report an error in this question

Data Warehousing & Data MiningHard

Q25. Decision tree classification in data mining:

  1. A.Builds a tree structure to classify data based on attribute values✓ Correct
  2. B.Creates random groupings of data without any logical partitioning
  3. C.Performs regression only for predicting continuous numeric values
  4. D.Finds association rules between items in transactional data sets

Explanation

Decision trees classify data by splitting on attribute values, creating a tree of decisions from root to leaf.

Report an error in this question

Data Warehousing & Data MiningHard

Q26. The K-means algorithm is a:

  1. A.Classification algorithm using labeled training data sets
  2. B.Association rule algorithm for finding item relationships
  3. C.Regression algorithm for predicting continuous value data
  4. D.Clustering algorithm that partitions data into K groups✓ Correct

Explanation

K-means is a popular clustering algorithm that partitions n data points into K clusters based on nearest mean.

Report an error in this question

Data Warehousing & Data MiningHard

Q27. A materialized view in a data warehouse is refreshed:

  1. A.Only when the database restarts after a scheduled shutdown
  2. B.Periodically or on demand to reflect source data changes✓ Correct
  3. C.In real-time always with zero latency from source updates
  4. D.Never after creation regardless of any source data changes

Explanation

Materialized views are refreshed periodically (incremental or full) to stay synchronized with source data.

Report an error in this question

Data Warehousing & Data MiningHard

Q28. Data lake differs from a data warehouse in that it:

  1. A.Is always smaller in size than a data warehouse
  2. B.Requires data transformation before storage occurs
  3. C.Stores raw, unstructured data in its native format✓ Correct
  4. D.Only stores structured data in normalized table form

Explanation

A data lake stores raw data in its native format (structured, semi-structured, unstructured) without prior transformation.

Report an error in this question

Data Warehousing & Data MiningHard

Q29. The concept of data lineage in data warehousing refers to:

  1. A.The size of tables measured in rows and storage bytes
  2. B.Tracking the origin and transformation history of data✓ Correct
  3. C.The number of queries executed against the data store
  4. D.The age of the database since its initial creation date

Explanation

Data lineage tracks where data comes from, how it has been transformed, and where it flows in the warehouse.

Report an error in this question

Data Warehousing & Data MiningHard

Q30. Real-time data warehousing aims to:

  1. A.Minimize the latency between source data changes and warehouse availability✓ Correct
  2. B.Store data offline in cold storage without any access for active queries
  3. C.Avoid using ETL processes entirely and load data without transformation
  4. D.Only process batch data on a periodic schedule without real-time updates

Explanation

Real-time data warehousing reduces the delay between operational data changes and their availability for analysis.

Report an error in this question

More Databases Topics

Introduction to Database Systems

Practice MCQs with explanations

Database Architecture

Practice MCQs with explanations

Data Models

Practice MCQs with explanations

Relational Database Concepts

Practice MCQs with explanations

Relational Algebra & Calculus

Practice MCQs with explanations

SQL

Practice MCQs with explanations

Ready to test yourself on Data Warehousing & Data Mining?

Take a timed quiz drawn from 210+ questions on this topic. No signup required — your progress saves in your browser.

Start Data Warehousing & Data Mining Quiz