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WorksheetsData warehouse exam
Total questions: 108
Worksheet time: 54mins
What is the primary purpose of a data warehouse?
Real-time business transactions
Data analysis for business decisions
Data validation during transactions
Data redundancy reduction
Which of the following is a characteristic of a data warehouse?
Transaction-oriented
Integrated data from multiple sources
Volatile data
Non-subject-oriented
Which type of data is organized around subjects in a data warehouse?
Transactional
Time-referenced
Non-volatile
Subject-oriented
What is the main goal of a data warehouse?
Provide easy access to corporate data
Support real-time business transactions
Minimize data redundancy
Enhance data validation during transactions
Which layer of the Data Warehouse Architecture is responsible for transporting data from source systems to the warehouse?
Metadata management layer
Data processing layer
Data integration layer
End user reporting layer
What is a characteristic of a snowflake schema in a dimensional model?
Complex dimensions are normalized
It consists of a central fact table
It is easy to understand and relate to business needs
It supports simplified business queries
What is the granularity of data?
The level of detail at which data is recorded
The size of the data warehouse
The number of dimensions in a star schema
The type of data stored in a warehouse
Which of the following is a true statement about additive facts in a data warehouse?
They cannot be added along any dimension
They are usually non-numeric values
They are always fully additive
They are typically numeric and additive along all dimensions
What is the purpose of a helper table in a dimensional model?
To store multi-valued dimensions
To handle complex hierarchies
To normalize complex dimensions
To simplify data retrieval processes
Which of the following is a characteristic of a dimensional model?
It uses an E-R diagram to represent relationships
It consists of a set of detailed business facts
It is optimized for real-time business transactions
It requires a normalized structure to minimize redundancy
Data warehouses are designed for:
Real-time business transactions
Analysis of historical data by subject area
Validating data during transactions
Supporting a high volume of concurrent users
Compared to an OLTP system, a data warehouse is optimized for:
A common and known set of transactions
Bulk loads and large complex, unpredictable queries
Few concurrent users
Minimal historical data
A dimensional model is designed to:
Minimize redundancy
Support simplified business queries
Validate input data
Provide real-time processing
In a star schema, the central table is called the:
Dimension table
Fact table
Measure table
Attribute table
A snowflake schema is a variation of a star schema where:
Dimensions are highly normalized
Facts are denormalized
Measures are aggregated
Attributes are consolidated
Granularity in a data warehouse refers to:
The level of detail at which data is recorded
The consistency of data
The security of data
The timeliness of data
A fact table is considered additive if adding it across a dimension results in a:
Non-meaningful value
Partially additive value
Fully additive value
Ratio value
Functional dependency of data in a dimensional model means:
Attributes depend on some, but not all, of the primary key
All attributes depend on the entire primary key
The primary key depends on all attributes
There is no relationship between attributes and the primary key
Helper tables in a dimensional model are used for:
Storing frequently used calculations
Representing multi-valued dimensions or complex hierarchies
Aggregating data at different granularities
Validating data quality
Hierarchies in a dimensional model can represent:
Different types of products
Geographic locations
Organizational structures
All of the above
Slowly Changing Dimensions (SCDs) are used to manage:
Changes to the structure of a dimension table
Historical values of dimension attributes
Both a) and b)
Neither a) nor b)
Conformed dimensions are used to:
Ensure consistency of dimension attributes across different data marts
Improve query performance for specific data marts
Denormalize data in the fact table
Reduce storage requirements
Data cleansing is the process of:
Extracting data from source systems
Identifying and correcting errors in data
Transforming data into a format suitable for the data warehouse
Loading data into the data warehouse
Data integration refers to:
Combining data from multiple sources into a consistent format
Identifying relationships between different data sets
Analyzing data to identify trends and patterns
Visualizing data for presentation
Data staging area is a temporary storage location used for:
Transforming and cleansing data before loading into the data warehouse
Archiving historical data from operational systems
Storing frequently accessed data for real-time queries
Providing a backup for the data warehouse
In a data warehouse ETL process, E stands for:
Extract
Transform
Load
All of the above
Data mining is the process of:
Storing and managing large datasets
Extracting hidden patterns and insights from data
Visualizing data for presentation
Designing and building data warehouses
A Kimball method dimensional model separates dimension tables from slowly changing dimension tables.
T
F
Invariant dimensions never change and have a single record for each unique value.
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F
Data warehouses are designed for real-time business transactions.
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F
Operational systems are optimized for bulk loads and large complex queries.
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F
The granularity of data refers to the level of detail at which data is recorded.
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F
A snowflake schema in a dimensional model is easy to understand and relate to business needs.
T
F
A fact is said to be fully additive if it is additive over every dimension of its dimensionality.
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F
Multi-valued dimensions in a dimensional model are handled using associative entities.
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F
Helper tables are used to normalize complex dimensions in a data warehouse.
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F
Functional dependency of data means that attributes within an entity are dependent on the primary key.
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F
A self-relationship involves one table, while a bill of materials involves two.
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F
The main goal of a data warehouse is to support real-time business transactions.
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F
Data warehouses are optimized for analysis of business measures by subject area, category, and attributes.
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F
Operational systems are designed for validation of data during transactions.
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F
Data in a data warehouse is organized around subjects, such as employees, accounts, and products.
T
F
Data warehouses are typically designed using a normalized structure to minimize redundancy.
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F
The granularity of data refers to its inherent cardinality.
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F
A fact is said to be partially additive if it is additive over at least one but not all of the dimensions.
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F
Helper tables are used to store multi-valued dimensions in a data warehouse.
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F
Functional dependency of data ensures non-redundancy in a data warehouse.
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F
A snowflake schema in a dimensional model is a denormalized structure.
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F
The granularity of data is determined by the number of attributes within an entity.
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F
Data warehouses are designed to support large user bases often distributed across geographies.
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F
A self-relationship in a dimensional model involves two tables.
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F
Data warehouses are optimized for data validation during transactions.
T
F
Multi-valued dimensions in a dimensional model are handled using self-relationships.
T
F
Data warehouses are designed to provide easy access to corporate data.
T
F
Operational systems are optimized for bulk loads and large complex queries.
T
F
The granularity of data refers to the level of detail at which data is recorded.
T
F
A snowflake schema in a dimensional model is easy to understand and relate to business needs.
T
F
A fact is said to be fully additive if it is additive over every dimension of its dimensionality.
T
F
Helper tables are used to normalize complex dimensions in a data warehouse.
T
F
Functional dependency of data means that attributes within an entity are dependent on the primary key.
T
F
A self-relationship involves one table, while a bill of materials involves two.
T
F
The main goal of a data warehouse is to support real-time business transactions.
T
F
Data warehouses are optimized for analysis of business measures by subject area, category, and attributes.
T
F
Operational systems are designed for validation of data during transactions.
T
F
Data in a data warehouse is organized around subjects, such as employees, accounts, and products.
T
F
Data warehouses are typically designed using a normalized structure to minimize redundancy.
T
F
The granularity of data refers to its inherent cardinality.
T
F
A fact is said to be partially additive if it is additive over at least one but not all of the dimensions.
T
F
Helper tables are used to store multi-valued dimensions in a data warehouse.
T
F
Data warehouses are a new invention in the field of data management.
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F
Data warehouses and OLTP systems serve similar purposes.
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F
A dimensional model is a type of normalized data model.
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F
The star schema is the most common type of dimensional model.
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F
In a snowflake schema, all dimensions are normalized to the same level.
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F
Increasing the granularity of data in a data warehouse will always improve query performance.
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F
Non-additive facts cannot be used in any analysis.
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F
Functional dependency ensures that there is no redundancy in a data warehouse.
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F
Helper tables are always optional in a dimensional model.
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F
Hierarchies can only be represented in snowflake schemas.
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F
Data warehouses are used to store and analyze large amounts of data for business intelligence purposes.
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F
Data warehouses are typically populated with data from operational systems.
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F
Data warehouses are designed for fast retrieval and analysis of data, rather than real-time processing.
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F
Dimensional models are easier to understand and use than traditional entity-relationship models for data warehousing.
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F
The fact table in a star schema stores the detailed business measures that are being analyzed.
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F
Dimensions in a star schema provide context and additional information about the facts.
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Snowflake schemas are more complex than star schemas but can improve query performance for complex queries.
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F
Data warehouses can store both historical and current data.
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F
The granularity of data in a data warehouse should be chosen based on the specific needs of the users.
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F
Additive facts allow for meaningful aggregation of data across different dimensions.
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Functional dependency helps to ensure data consistency in a data warehouse.
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F
Helper tables can be used to model complex relationships between dimensions.
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Hierarchies allow users to drill down and roll up data at different levels of detail.
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F
Data warehouses are a valuable tool for businesses that need to make data-driven decisions.
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F
Data warehouses can be used for real-time operational reporting.
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F
Data warehouses are typically updated in batch mode.
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F
Data marts are subsets of a data warehouse focused on specific business needs.
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F
Data virtualization provides a logical view of data from various sources without physically moving the data.
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F
In-memory data warehouses offer faster query performance but require more expensive hardware.
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F
Data governance establishes policies and procedures for managing data throughout its lifecycle.
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Data quality is critical for accurate and reliable analysis in data warehouses.
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Data security protects sensitive data from unauthorized access.
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Data privacy regulations govern the collection, storage, and use of personal data.
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Data warehouses are a crucial component for business intelligence and data analytics.
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F
A dimension table can have a foreign key relationship with the fact table.
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F
A fact table can have multiple foreign key relationships to the same dimension table.
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F
Surrogate keys are unique identifiers used in dimension tables to improve query performance.
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F
Degeneracy occurs when some dimension attributes are missing from a dimension table record.
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F
Outliers are data points that fall significantly outside the expected range.
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F
