Data Management Foundations (C175 )
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Free Data Management Foundations (C175 ) Questions
What is the role of description in Business Intelligence data processing
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Assigning numeric values to objects
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Characterizing discovered data
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Dividing entities based on similarity
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Evaluating relationships between data elements
Explanation
Correct Answer B. Characterizing discovered data
Explanation
Description in Business Intelligence data processing involves summarizing and characterizing the data to understand its properties and insights better. This step helps in interpreting the data and identifying patterns or trends for informed decision-making.
Why other options are wrong
A. Assigning numeric values to objects is part of estimation, not description. Description focuses on interpreting data, not assigning values.
C. Dividing entities based on similarity is the purpose of clustering, which groups similar data points rather than describing their characteristics.
D. Evaluating relationships between data elements is related to correlation or association, not the process of description, which focuses on summarizing and explaining data.
What is the process involved in improving data accessibility in Business Intelligence
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Volume, performance, description, affinity grouping
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Volume, performance, clustering, estimation
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Volume, performance, regression, clustering
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Volume, performance, lineage, speed of delivery
Explanation
Correct Answer D. Volume, performance, lineage, speed of delivery
Explanation
Improving data accessibility in Business Intelligence involves considering the volume of data being handled, ensuring high performance for queries and analysis, maintaining data lineage (tracking the origin and changes to data), and enhancing the speed of data delivery to end-users. These factors ensure data is both accessible and reliable for decision-making.
Why other options are wrong
A. Description and affinity grouping are not processes directly tied to improving data accessibility. While they are relevant to data analysis, they do not address the critical aspects of speed, lineage, and performance required to improve accessibility.
B. Clustering and estimation are analytical techniques used to extract patterns or predictions from data. They are not part of the infrastructure or processes involved in making data more accessible for end-users.
C. Regression and clustering focus on data analysis rather than data accessibility. These methods are used to identify relationships and patterns but do not impact how easily data can be accessed or delivered.
What is the dimensional model's role in Business Intelligence data warehousing
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The dimensional model complicates data reporting requests.
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The dimensional model limits data extensibility.
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The dimensional model ensures bias in data analysis.
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The dimensional model focuses on data profiling.
Explanation
Correct Answer D. The dimensional model focuses on data profiling.
Explanation
The dimensional model is designed to make data easier to understand and analyze by structuring it into dimensions and facts. It plays a critical role in data profiling, which involves understanding the data's content, structure, and relationships to facilitate effective analysis in Business Intelligence.
Why other options are wrong
A. The dimensional model does not complicate data reporting requests. Instead, it simplifies data organization and reporting by using structures such as dimensions and facts to make data analysis straightforward. This simplification is one of its primary goals.
B. The dimensional model does not limit data extensibility. It is designed to allow flexibility, making it easier to add new dimensions or measures as business needs evolve. This adaptability ensures that it can grow with the organization.
C. The dimensional model does not ensure bias in data analysis. Its purpose is to organize data in a way that promotes accurate and unbiased reporting. Any bias would typically stem from data collection or interpretation, not from the model itself.
What does explanatory analytics employ to create actionable predictive models based on available data
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Simple data gathering and transformation
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Mathematical and statistical algorithms, neural networks, artificial intelligence, and other advanced modeling tools
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Business decision making
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Data storage and retrieval
Explanation
Correct Answer B. Mathematical and statistical algorithms, neural networks, artificial intelligence, and other advanced modeling tools
Explanation
Explanatory analytics employs advanced modeling techniques, including mathematical and statistical algorithms, neural networks, and artificial intelligence, to create actionable predictive models. These tools enable organizations to uncover hidden patterns and trends in data for informed decision-making.
Why other options are wrong
A. Simple data gathering and transformation: While essential for preparing data, these tasks do not create predictive models; they are preliminary steps in the analytics process.
C. Business decision making: Decision-making is the result of explanatory analytics, but the process itself uses advanced algorithms and models to provide insights.
D. Data storage and retrieval: Storage and retrieval are foundational database functions, not tasks related to creating predictive models.
What defines a table that is said to be in 2NF
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Has all attributes defined
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Has no repeating groups
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Has all key attributes defined, no repeating groups, and all attributes dependent on the primary key
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Has no dependencies
Explanation
Correct Answer C. Has all key attributes defined, no repeating groups, and all attributes dependent on the primary key
Explanation
A table is in 2NF if it meets all the criteria of 1NF and additionally eliminates partial dependencies. This means that all non-key attributes must be fully functionally dependent on the entire primary key.
Why other options are wrong
A. Has all attributes defined: While having attributes defined is a necessary condition, it does not satisfy the specific requirements for 2NF, such as the removal of partial dependencies.
B. Has no repeating groups: This is a requirement for 1NF, not 2NF. 2NF goes further by addressing partial dependencies.
D. Has no dependencies: This is incorrect because even in 2NF, there can still be transitive dependencies. The goal of 2NF is to address partial dependencies, not all dependencies.
What does CASCADE do in a database system with referential integrity violations
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Rejects the operation
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Sets invalid keys to NULL
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Propagates primary key changes
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Sets invalid keys to a default value
Explanation
Correct Answer C. Propagates primary key changes
Explanation
CASCADE in a database system ensures that when a primary key is updated or deleted, the corresponding foreign key values in related tables are automatically updated or deleted. This helps maintain referential integrity by propagating the changes throughout the database.
Why other options are wrong
A. Rejects the operation
Rejecting the operation is the behavior of the RESTRICT option, not CASCADE. CASCADE allows the operation and propagates changes.
B. Sets invalid keys to NULL
Setting invalid keys to NULL is the behavior of the SET NULL option, not CASCADE. CASCADE modifies the foreign key values to match the primary key operation instead of setting them to NULL.
D. Sets invalid keys to a default value
Setting invalid keys to a default value is handled by the SET DEFAULT option, not CASCADE. CASCADE directly updates or deletes the foreign key values to maintain consistency.
What are the basic building blocks of a data model
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Entities, Attributes, Relationships, Constraints
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Attributes, Relationships, Constraints
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Entities, Relationships, Constraints
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Entities, Attributes, Relationships
Explanation
Correct Answer A. Entities, Attributes, Relationships, Constraints
Explanation
The basic building blocks of a data model are entities (objects or things of interest), attributes (characteristics of entities), relationships (associations between entities), and constraints (rules that limit or define the data). Together, these components define how data is structured and interact within a system.
Why other options are wrong
B. Attributes, Relationships, Constraints
This option omits entities, which are a fundamental part of a data model.
C. Entities, Relationships, Constraints
While entities, relationships, and constraints are important, this option leaves out attributes, which describe the properties of entities.
D. Entities, Attributes, Relationships
This option excludes constraints, which are necessary to enforce rules and maintain data integrity.
What is the condition in which each row in a table has its own unique identity
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Data Redundancy
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Entity Integrity
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Referential Integrity
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Data Integrity
Explanation
Correct Answer B. Entity Integrity
Explanation
Entity integrity is a condition where each row in a table is uniquely identifiable, usually enforced by a primary key. This ensures that no two rows have the same identity and maintains the uniqueness of each record.
Why other options are wrong
A. Data redundancy refers to unnecessary duplication of data, which is unrelated to the unique identification of rows.
C. Referential integrity ensures that foreign keys in a table reference valid rows in another table but does not address the unique identity of rows in a single table.
D. Data integrity is a broader concept that includes entity integrity but is not specific to unique row identities.
How is the one to many (1:M) relationship implemented in the relational model
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Not Sure
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True
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Maybe
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False
Explanation
Correct Answer B. True
Explanation
In the relational model, a one-to-many (1:M) relationship is implemented by linking a primary key in the "one" table to a foreign key in the "many" table. This setup ensures that each record in the "one" table can be associated with multiple records in the "many" table, maintaining relational integrity.
Why other options are wrong
A. Not Sure: This does not provide a definitive or meaningful answer to the question.
C. Maybe: The implementation of a 1:M relationship is a well-established practice, so "maybe" is not a valid response.
D. False: It is incorrect to claim that the 1:M relationship cannot be implemented in the relational model, as it is a fundamental concept in relational databases.
What is the process of creating a specific data model for a determined problem domain
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Data Warehousing
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Data Management
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Data Analysis
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Data Modeling
Explanation
Correct Answer D. Data Modeling
Explanation
Data modeling is the process of creating a specific structure or framework for how data is organized and related within a particular problem domain. This process ensures that the data supports the requirements of the application or system.
Why other options are wrong
A. Data warehousing refers to collecting and managing data from multiple sources for analytical purposes but does not involve creating specific models for problem domains.
B. Data management focuses on the collection, storage, and retrieval of data, rather than defining its structure for a specific problem.
C. Data analysis involves interpreting and extracting insights from data, not creating data models.
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