WorksheetsData Warehousing and OLAP Worksheet
Total questions: 106
Worksheet time: 53mins
Text mining mainly deals with
A) Image data
B) Video data
C) Unstructured text data
D) Numerical data
Tokenization means
A) Removing stop words
B) Splitting text into words
C) Stemming words
D) Counting frequency
Which is a stop word?
A) Mining
B) Data
C) The
D) Algorithm
Stemming converts
A) Run → running
B) Running → run
C) Data → dataset
D) Text → texts
Lemmatization differs from stemming because it
A) Ignores grammar
B) Uses dictionary meaning
C) Removes punctuation
D) Counts words
TF in TF-IDF stands for
A) Term File
B) Term Frequency
C) Text Factor
D) Term Function
A retail company initially designed a Star Schema for its data warehouse but later decided to normalize its dimension tables for better storage optimization. What type of schema does the company now use?
Star Schema
Snowflake Schema
Fact Constellation Schema
Hybrid Schema
A word appearing in many documents will have
A) High IDF
B) Low IDF
C) High TF
D) Zero TF
A shopping website records customer transactions, including Order ID, Product ID, Quantity, and Revenue. What type of table should store this transactional data?
Fact Table
Dimension Table
Lookup Table
Staging Table
Web mining extracts information from
A) Databases
B) Warehouses
C) Web data
D) Sensors
Web content mining deals with
A) Page structure
B) Text, images, videos
C) Hyperlinks
D) User logs
Web structure mining focuses on
A) Page text
B) HTML tags
C) Hyperlinks
D) Cookies
Which OLAP operation allows the manager to view data in more detail, focusing on a specific region?
Drill-down
Roll-up
Slice
Pivot
In a data warehouse, which type of table stores historical data and is used to track changes over time?
Fact Table
Dimension Table
Slowly Changing Dimension Table (SCD)
Lookup Table
A company needs to design a schema where dimension tables are highly normalized to reduce redundancy. Which schema is best suited for this requirement?
Star Schema
Snowflake Schema
Fact Constellation Schema
Galaxy Schema
Web usage mining analyzes
A) Web servers
B) User behavior
C) HTML code
D) URLs
Automatic classification of web documents is an example of
A) Clustering
B) Indexing
C) Supervised learning
D) Sorting
Naive Bayes is used for
A) Regression
B) Classification
C) Sorting
D) Searching
Spam detection is an example of
A) Clustering
B) Classification
C) Tokenization
D) Parsing
Which is NOT a preprocessing step?
A) Tokenization
B) Stop word removal
C) Normalization
D) Encryption
HTML tags help in
A) Content mining
B) Page layout structure
C) Image mining
D) Usage mining
A bag-of-words model ignores
A) Word frequency
B) Grammar and word order
C) Stop words
D) Documents
Which of the following best describes the role of an OLAP cube in data warehousing? (CO1)
It stores raw transaction data
It allows for quick querying and analysis of multi-dimensional data
It normalizes the dimension tables for better storage
It transforms data from external sources to internal formats
Which of the following operations would most likely be performed during the 'Transform' phase of ETL? (CO1)
Extracting data from operational databases
Aggregating data for reporting
Storing data in the data warehouse
Cleaning and filtering data to match the data model
Which of the following is an advantage of using a star schema over a snowflake schema? (CO1)
It reduces redundancy in dimension tables
It is easier to implement and more intuitive for querying
It is better at handling many-to-many relationships
It results in more normalized data
Which of the following is a major disadvantage of using the snowflake schema in data warehousing? (CO1)
Increased query complexity due to additional joins
Increased storage space due to denormalization
Inability to handle large data volumes
Poor support for multi-dimensional analysis
In the context of a data warehouse, a data mart is: (CO1)
A subset of the data warehouse designed for a specific business function
A tool used to perform analytical queries
A method for normalizing data for storage
A type of OLAP cube for multi-dimensional analysis
Which of the following is true about OLAP (Online Analytical Processing)? (CO1)
OLAP systems are designed for real-time transactional processing
OLAP systems use a multidimensional model to analyze data
OLAP systems are primarily used for storing large volumes of data
OLAP systems focus on managing day-to-day operations
In a data warehouse, the process of "drill-down" refers to: (CO1)
Aggregating data at a higher level of granularity
Breaking down data into more detailed levels of granularity
Querying data from a specific time period
Summarizing data into key performance indicators (KPIs)
Which of the following describes the main function of a data cube in OLAP? (CO1)
To optimize ETL processes
To enable multidimensional data querying and analysis
To store operational transaction data
To perform real-time processing of data
What does the "Extract" phase in the ETL process primarily involve? (CO1)
Filtering data to meet business needs
Aggregating data into summary formats
Fetching data from various source systems
Storing data in the data warehouse
In dimensional modeling, which of the following is an example of a "dimension"? (CO1)
Sales revenue
Customer
Sales transaction
Product quantity
In a sales dataset with dimensions for Time (Day, Week, Month), Product (Category, Sub-category), and Region (Country, City), you wish to compare total sales for different regions within the same product category for a specific month. Which OLAP operation would you use? (CO1)
Roll-up
Dicing
Drill-down
Slice
You are working with a data cube that contains dimensions for Time, Product, and Geography. To view sales data for a specific year, across all products and regions, which OLAP operation would you use? (CO1)
Slice
Drill-down
Roll-up
Dicing
In an OLAP system, if you want to navigate from a high-level overview (e.g., total sales) to a detailed breakdown (e.g., sales by product or region), which OLAP operation would you use? (CO1)
Slice
Drill-down
Roll-up
Pivot
A user wants to examine sales for a product across multiple regions, but only for a specific time period (e.g., January 2024). Which OLAP operation would you apply? (CO1)
Slice
Pivot
Drill-down
Roll-up
When analyzing sales data, you decide to view the total sales of a product category for the entire year, summarized by region. What OLAP operation is being performed? (CO1)
Slice
Drill-down
Roll-up
Dicing
If a user wants to compare total sales across multiple regions for a specific product category and time period, which OLAP operation should be used?
Drill-down
Pivot
Slice
Roll-up
Which OLAP operation is used to focus on a specific subset of data by selecting a single value for one dimension while keeping the other dimensions fixed?
Slice
Roll-up
Pivot
Drill-down
Which of the following describes the relationship between fact tables and dimension tables in a star schema?
The fact table contains multiple foreign keys, each referring to a primary key in a dimension table.
The fact table has one-to-one relationships with each dimension table.
The dimension tables contain foreign keys that refer to the fact table.
Fact tables do not have any relationship with dimension tables.
During the ETL process, when data is being loaded into the data warehouse, which of the following actions is typically performed?
Data transformation rules are applied.
Data is aggregated to generate summaries.
Data is directly written to the fact table without checking for duplicates.
Data is stored in the staging area temporarily.
Which of the following best describes the Extract phase of the ETL process?
Aggregating data into a consistent format.
Loading transformed data into the target data warehouse.
Retrieving raw data from multiple source systems.
Validating and cleaning the data for transformation.
A fact table in a data warehouse typically contains:
Descriptive attributes like customer name, product description, etc.
Quantitative data such as sales revenue, units sold, etc.
Hierarchical relationships between dimensions.
A set of foreign keys linking to dimension tables, but no actual numeric data.
Which of the following statements best describes a Data Mart?
A Data Mart is a centralized repository for the entire organization's data.
A Data Mart is a subset of a Data Warehouse, typically focused on a specific business area.
A Data Mart contains raw, unprocessed data only.
A Data Mart is used exclusively for storing transactional data.
Which of the following is true about dependent Data Marts?
They are built independently of the central Data Warehouse.
They contain data that is not linked to the central Data Warehouse.
They pull data directly from the central Data Warehouse and focus on a specific business area.
They do not support OLAP operations.
Which of the following best describes a star schema in dimensional data modeling?
The fact table is normalized.
The dimension tables are normalized.
The fact table is denormalized, and dimension tables are usually not normalized.
The schema requires multiple joins for efficient query processing.
Which of the following is a key disadvantage of using a star schema?
Increased complexity in query processing.
Data redundancy in dimension tables.
Requires more storage space compared to the snowflake schema.
Difficulty in maintaining data consistency across dimension tables.
In a snowflake schema, the dimension tables are typically:
Denormalized to reduce data redundancy.
Fully normalized into multiple related tables.
Stored in separate schemas from the fact table.
Stored as views, not tables.
Which of the following best describes the main advantage of a snowflake schema over a star schema?
Better query performance due to fewer joins.
Better space utilization due to normalization.
Reduced complexity in the fact table.
Easier for OLAP systems to process.
In a star schema, the fact table typically contains:
Primary keys of the dimension tables.
Descriptive attributes like customer names.
Hierarchical relationships between dimensions.
Calculated values for aggregated data.
In a snowflake schema, which of the following is true about the relationships between dimension tables?
Dimension tables are normalized into several smaller tables.
Each dimension table is independent and not linked to other tables.
Dimension tables are denormalized for faster querying.
All dimension tables contain fact data.
What is the primary purpose of using a fact table in dimensional data modeling?
To store descriptive attributes like customer names and product categories.
To store the transactional or quantitative data (e.g., sales revenue, quantity sold).
To store time-related data for analysis.
To store foreign keys of related dimension tables.
In a dimensional data model, what does the "drill-down" operation refer to?
Aggregating data to a higher level of granularity.
Filtering data to focus on a specific subset.
Navigating from summarized data to more detailed data.
Pivoting the data to examine it from different angles.
Which of the following is a characteristic of OLAP query processing?
OLAP queries focus on retrieving and updating operational data.
OLAP queries perform complex calculations and aggregations on multi-dimensional data.
OLAP queries are optimized for transactional processing.
OLAP queries are designed for low-latency, real-time data retrieval.
What is a common characteristic of both the star and snowflake schema in dimensional data modeling? (CO1)
Both schemas involve multiple fact tables.
Both schemas store only summarized data.
Both schemas require the use of foreign keys to link dimension tables to fact tables.
Both schemas store normalized data in the dimension tables.
Which of the following is a key benefit of using a star schema over a snowflake schema? (CO1)
Faster query performance due to fewer joins between tables.
Easier to maintain data consistency across multiple tables.
Better support for historical data and trends.
More normalized data structures for better storage efficiency.
What does the "roll-up" operation in OLAP allow users to do? (CO1)
Navigate from detailed data to a summarized or higher level of granularity.
Filter the data based on certain conditions or criteria.
View data across different dimensions simultaneously.
Drill into more granular levels of data.
In dimensional data modeling, which of the following describes a "dimension"? (CO1)
A fact table containing quantitative data.
An attribute or category by which data can be segmented (e.g., product, time, location).
A set of foreign keys linking the fact table to dimension tables.
A set of aggregated values representing key metrics.
What is the main purpose of using dimension tables in a dimensional data model? (CO1)
To store transactional data for analysis.
To provide context for analyzing the quantitative data in the fact table.
To store aggregated summary data.
To store multi-dimensional data for OLAP analysis.
Which of the following best describes the main difference between OLTP and OLAP systems? (CO1)
OLTP is designed for large-scale analytical queries, while OLAP is designed for transaction-based operations.
OLTP is designed for real-time transactional data, while OLAP is designed for complex data analysis and reporting.
OLTP systems do not support data analysis, whereas OLAP systems focus on small-scale transaction processing.
OLAP systems process real-time data, while OLTP handles batch processing.
Which of the following operations would typically be performed in an OLAP system but not in an OLTP system? (CO1)
Inserting new records
Deleting records
Aggregating and summarizing data
Updating transactional data
Which of the following is most commonly stored in an OLTP system? (CO1)
Data for analysis and reporting purposes
Historical data aggregated for business insights
Real-time transactional data such as sales orders or inventory updates
Summarized data used for decision-making
OLAP systems are optimized for which of the following? (CO1)
Insert, update, and delete operations
Transactional operations with a focus on speed
Complex queries for business intelligence and trend analysis
Real-time processing of operational data
Which of the following is a key characteristic of OLTP systems? (CO1)
Large volumes of data processed at once
Complex queries that require aggregation
Simple queries that deal with small records and fast transaction times
Data is often historical and used for reporting purposes
When comparing OLTP and OLAP, which statement is true about OLAP systems? (CO1)
OLAP systems support detailed and high-frequency transactions.
OLAP systems handle millions of small transactions per second.
OLAP systems are designed for performing complex analyses and aggregations on large volumes of data.
OLAP systems are optimized for data entry and transactional consistency.
Which of the following is a characteristic of OLTP systems in terms of data volume and processing? (CO1)
High data volume with large historical datasets
Low data volume with frequent updates, inserts, and deletes
Data is generally aggregated for reporting and analysis
Complex multi-dimensional analysis
Which of the following operations would likely be done in an OLTP system rather than an OLAP system? (CO1)
Running a report showing sales for the last five years
Inserting a new customer order into the database
Aggregating data for performance analysis
Performing trend analysis based on historical data
Which of the following systems would most likely use a star or snowflake schema? (CO1)
OLTP systems for transactional processing
OLAP systems for data analysis and reporting
OLTP systems for handling inventory data
OLAP systems for real-time processing of orders
Which type of system is most likely to use a large number of transactions per second with a focus on real-time processing? (CO1)
OLTP system
OLAP system
Both OLTP and OLAP systems
Neither OLTP nor OLAP systems
Which of the following best describes an Operational System? (CO1)
A system designed for long-term strategic decision-making and complex queries.
A system designed to handle routine transactions and day-to-day operations.
A system used for analyzing historical data for decision support.
A system designed for reporting purposes and aggregating data.
Which of the following is a key feature of Decision Support Systems (DSS)? (CO1)
They are designed to handle routine, day-to-day business operations.
They provide real-time data processing for operational needs.
They assist in complex decision-making by analyzing large datasets.
They focus on transactional processing and operational efficiency.
Which of the following is the primary goal of an Operational System? (CO1)
To assist in decision-making and strategic planning.
To process large volumes of data and generate complex reports.
To process and record day-to-day business transactions.
To analyze historical trends for forecasting and budgeting.
Which system is typically used for activities like order processing, inventory management, and payroll? (CO1)
Decision Support System (DSS)
Operational Support System (OSS)
Executive Information System (EIS)
Transaction Processing System (TPS)
Which of the following would NOT typically be a function of a Decision Support System (DSS)? (CO1)
Analyzing trends and making predictions based on historical data.
Generating routine operational reports for day-to-day management.
Evaluating the impact of different strategies on business performance.
Supporting decisions related to resource allocation and budgeting.
Which of the following systems is designed to support the operational level of an organization? (CO1)
Decision Support System (DSS)
Executive Information System (EIS)
Transaction Processing System (TPS)
Knowledge Management System (KMS)
Which of the following is a primary characteristic of Operational Systems? (CO1)
Focused on data aggregation and analysis for decision-making.
Handles real-time data for routine business processes.
Supports high-level strategic decision-making for executives.
Designed for managing large datasets for long-term forecasting.
What is the main focus of Decision Support Systems (DSS)? (CO1)
Recording daily transactions and operational activities.
Analyzing data to help managers make informed decisions.
Optimizing operational workflows and enhancing efficiency.
Ensuring the consistency of transactional data.
Which of the following best describes the primary function of an Operational Support System (OSS)? (CO1)
To assist in data analysis and decision-making for upper management.
To manage and execute the day-to-day activities and operations of an organization.
To track and analyze financial data for long-term planning.
To assist in reporting and analysis for strategic decision-making.
In which type of system would you typically find reports such as sales data, inventory levels, and customer orders? (CO1)
Decision Support System (DSS)
Executive Information System (EIS)
Operational Support System (OSS)
Transaction Processing System (TPS)
Which system is primarily concerned with helping managers analyze information for decision-making and problem-solving? (CO1)
Transaction Processing System (TPS)
Executive Information System (EIS)
Decision Support System (DSS)
Knowledge Management System (KMS)
Which of the following is NOT typically a function of an Operational System? (CO1)
Processing daily transactions such as sales, inventory, and customer orders.
Generating operational reports for immediate management decisions.
Supporting real-time decision-making by top-level executives.
Maintaining the integrity of transactional data for reporting and compliance.
Which of the following is an example of how a Decision Support System (DSS) is used? (CO1)
Analyzing sales trends over a period of time to forecast future demand.
Recording customer orders as they are placed in real time.
Processing payroll and employee data for day-to-day operations.
Managing the daily inventory levels for a retail store.
Which system would be most appropriate for tracking inventory in real-time during store operations? (CO1)
Transaction Processing System (TPS)
Decision Support System (DSS)
Knowledge Management System (KMS)
Executive Information System (EIS)
Which of the following is a key difference between Operational Systems and Decision Support Systems? (CO1)
Operational Systems process real-time data, while Decision Support Systems analyze historical data for insights.
Operational Systems focus on strategic decision-making, while DSS supports transactional operations.
Operational Systems aggregate data for long-term planning, while DSS performs real-time processing.
Operational Systems are used only for reporting, while DSS manages transactions.
Which of the following is the primary goal of a data warehouse? (CO1)
To store real-time transactional data.
To support decision-making processes by storing historical data.
To manage operational data for daily business operations.
To store unstructured data for analysis.
Which of the following is NOT a characteristic of a Data Warehouse? (CO1)
It supports decision-making and data analysis.
It integrates data from multiple sources.
It stores real-time transactional data.
It stores historical and aggregated data.
Which architecture is typically used for implementing a data warehouse? (CO1)
Client-server architecture
Two-tier architecture
Three-tier architecture
Peer-to-peer architecture
Which of the following is a component of the Data Warehouse architecture? (CO1)
Data mart
Operational database
Data staging area
Data lakes
Which of the following processes is NOT part of ETL (Extract, Transform, Load) in a Data Warehouse? (CO1)
Extracting data from source systems
Transforming data into a usable format
Storing real-time transactional data
Loading transformed data into the data warehouse
In a Data Warehouse, what is the purpose of the staging area? (CO1)
To store final processed data used for reporting
To perform complex queries on operational data
To temporarily store data before it is cleaned and transformed
To store aggregated data for decision-making
Which of the following is a feature of a Data Warehouse that differentiates it from an OLTP system? (CO1)
It stores real-time transactional data.
It supports decision-making by providing historical data and trends.
It processes millions of small transactions per second.
It focuses on updating data in real time.
What is the primary function of a Data Mart in a Data Warehouse architecture? (CO1)
To perform ETL operations for the entire organization
To store detailed data for operational reporting
To store a subset of the data warehouse tailored to a specific business unit
To store unstructured data used for analysis
Which of the following is the first step in the ETL process? (CO1)
Data Transformation
Data Loading
Data Extraction
Data Cleaning
Which of the following is NOT typically a source for data loading into a Data Warehouse? (CO1)
Operational databases
External data sources
Data marts
Transactional applications
Which of the following is a key feature of the architecture of a Data Warehouse? (CO1)
It supports only real-time transactional processing.
It separates data storage and processing into different layers.
It integrates only data from a single source.
It does not include an operational database for transaction management.
In the context of Data Warehousing, what does the term "Data Granularity" refer to? (CO1)
The level of detail or summarization of the data stored in the warehouse
The speed at which data is processed in the warehouse
The size of the data stored in the warehouse
The structure and format of the data stored in the warehouse
Which of the following types of schema is commonly used in a Data Warehouse? (CO1)
Relational schema
Star schema
Object-oriented schema
Hierarchical schema
Which of the following is an advantage of using a Data Warehouse for business analysis? (CO1)
Provides real-time processing of operational transactions
Consolidates data from multiple sources for easy reporting and analysis
Supports immediate updates of operational systems
Provides transactional data with minimal transformation
Which of the following is a disadvantage of using a Data Warehouse? (CO1)
It allows real-time access to transactional data.
It can require significant storage and processing power.
It is less complex compared to OLTP systems.
It only supports operational reporting, not decision support.
__ system is market oriented and is used for data analysis by knowledge workers including Managers, Executives, and Analysts. (CO1)
OLTP
OLAP
Data System
Market System
Which of the following is NOT a major functionality of data mining? (CO2)
Association Rule Mining
Classification
Data Encryption
Clustering
Which of the following is the correct sequence of steps in the KDD (Knowledge Discovery in Databases) process? 1. Data Selection 2. Data Preprocessing 3. Data Transformation 4. Data Mining 5. Pattern Evaluation (CO2)
1 → 2 → 3 → 4 → 5
2 → 3 → 1 → 4 → 5
1 → 3 → 2 → 4 → 5
1 → 2 → 4 → 3 → 5
A dataset contains 10,000 records, and a classification algorithm is applied to predict whether a customer will purchase a product. The classification accuracy obtained is 85%. How many instances are incorrectly classified? (CO2)
850
1,500
2,000
3,000
A dataset contains 5% missing values in a column. Which of the following methods is NOT an appropriate technique for handling missing values? (CO2)
Deleting rows with missing values
Replacing missing values using mean or median
Replacing missing values with randomly generated values
Using regression-based imputation
Min-Max normalization is applied to a dataset with values ranging from 50 to 250, converting them into a range [0,1]. What will be the normalized value of 150? (CO2)
0.25
0.50
0.75
1.00
Which of the following sampling techniques is most effective for reducing biased representation in large datasets? (CO2)
Random Sampling
Stratified Sampling
Cluster Sampling
Systematic Sampling
