[03/Tennsift/01]
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Questions and Answers

What is a possible target model that can be derived from a valid Raw Data Vault model?

  • Hierarchical model
  • Dimensional model (star schema, snowflake schema) (correct)
  • Network model
  • Entity-relationship model

What is a key characteristic of a valid model for maintaining data consistency?

  • Ability to store data in any format without constraints
  • Ability to prioritize speed over data integrity
  • Ability to generate artificial data without loss
  • Ability to reproduce every single delivery to the data warehouse without losing data (correct)

Apart from a dimensional model, what is another possible target model that can be derived from a valid Raw Data Vault model?

  • Flat file model
  • NoSQL document model
  • Third-normal form model (correct)
  • Key-value store model

True or false: Valid models are not important for maintaining data consistency?

<p>False (B)</p> Signup and view all the answers

True or false: A valid Raw Data Vault model can only derive a dimensional model as a target model?

<p>False (B)</p> Signup and view all the answers

True or false: Third-normal form is not a possible target model that can be derived from a valid Raw Data Vault model?

<p>False (B)</p> Signup and view all the answers

Match the following target models with their possible derivation from a valid Raw Data Vault model:

<p>Dimensional model (star schema, snowflake schema) = Possible target model from a valid Raw Data Vault model Third-normal form = Possible target model from a valid Raw Data Vault model Better Data Vault model = Possible target model from a valid Raw Data Vault model Artificially generated data = Not a possible target model from a valid Raw Data Vault model</p> Signup and view all the answers

Match the following characteristics with a valid model for maintaining data consistency:

<p>Enables reproduction of every single delivery to the data warehouse without data loss = Characteristic of a valid model for maintaining data consistency Does not artificially generate data = Characteristic of a valid model for maintaining data consistency Primarily used for client-side scripting in web applications = Not a characteristic of a valid model for maintaining data consistency Used for styling web pages = Not a characteristic of a valid model for maintaining data consistency</p> Signup and view all the answers

Match the following statements with their relation to valid models:

<p>Valid models are key to maintaining data consistency = Statement related to valid models Valid models are unnecessary for data consistency = Statement not related to valid models Valid models are primarily used for general-purpose programming = Statement not related to valid models Valid models are only used for database queries = Statement not related to valid models</p> Signup and view all the answers

What is a key method to ensure data consistency in data modeling?

<p>Defining clear data standards and rules (B)</p> Signup and view all the answers

What does data consistency in data modeling help to improve?

<p>Quality of data analysis and reporting (C)</p> Signup and view all the answers

What does implementing data integrity constraints involve?

<p>Using database constraints to prevent data inconsistencies (B)</p> Signup and view all the answers

What is the purpose of monitoring data quality in data modeling?

<p>Identify and fix any inconsistencies (A)</p> Signup and view all the answers

How does data consistency in data modeling reduce the risk of errors in decision-making?

<p>By ensuring reliable and error-free data (C)</p> Signup and view all the answers

What is a common method to ensure data consistency in data modeling?

<p>Using data validation techniques (D)</p> Signup and view all the answers

Why is it important to ensure data consistency in data modeling?

<p>To enhance the trust of users in the data (A)</p> Signup and view all the answers

Data consistency in data modeling is the degree to which the data in a data model is accurate, reliable, and free from errors or conflicts.

<p>True (A)</p> Signup and view all the answers

It is not important to ensure data consistency in data modeling because it does not impact data analysis and reporting.

<p>False (B)</p> Signup and view all the answers

Using data validation techniques involves checking the data to ensure that it is complete, accurate, and consistent with the defined standards and rules.

<p>True (A)</p> Signup and view all the answers

Implementing data integrity constraints involves using database constraints to prevent data inconsistencies from occurring in the first place.

<p>True (A)</p> Signup and view all the answers

Monitoring data quality involves regularly checking the data to identify and fix any inconsistencies that may arise.

<p>True (A)</p> Signup and view all the answers

Defining clear data standards and rules includes defining the format, structure, naming, quality, and security of the data elements and attributes in a data model.

<p>True (A)</p> Signup and view all the answers

Facilitating the integration of data from different sources is not a benefit of ensuring data consistency in data modeling.

<p>False (B)</p> Signup and view all the answers

Match the following methods with their purpose in ensuring data consistency in data modeling:

<p>Defining clear data standards and rules = Defining the format, structure, naming, quality, and security of the data elements and attributes Using data validation techniques = Checking the data to ensure completeness, accuracy, and consistency with defined standards and rules Implementing data integrity constraints = Using database constraints to prevent data inconsistencies from occurring Monitoring data quality = Regularly checking the data to identify and fix any inconsistencies</p> Signup and view all the answers

Match the following benefits with the importance of data consistency in data modeling:

<p>Improve the quality of data analysis and reporting = Enhances trust of users in the data Reduce the risk of errors in decision-making = Facilitate the integration of data from different sources Enhance the trust of users in the data = Improve the quality of data analysis and reporting Facilitate the integration of data from different sources = Reduce the risk of errors in decision-making</p> Signup and view all the answers

Match the following statements with their relation to ensuring data consistency in data modeling:

<p>Data consistency helps improve the quality of data analysis and reporting = Accurate and reliable data is essential for quality analysis and reporting Ensuring data consistency reduces the risk of errors in decision-making = Inconsistent data can lead to erroneous decision-making Facilitating the integration of data from different sources is a benefit of ensuring data consistency = Consistent data enables seamless integration from various sources Improving the trust of users in the data is a result of ensuring data consistency = Consistent and accurate data builds trust among users</p> Signup and view all the answers

Match the following characteristics with the methods to ensure data consistency in data modeling:

<p>Format, structure, naming, quality, and security = Defining clear data standards and rules Completeness, accuracy, and consistency = Using data validation techniques Preventing data inconsistencies from occurring = Implementing data integrity constraints Regularly identifying and fixing inconsistencies = Monitoring data quality</p> Signup and view all the answers

Match the following reasons with the importance of data consistency in data modeling:

<p>Improving data analysis and reporting = Importance of accurate and reliable data for analysis and reporting Reducing errors in decision-making = Critical for making informed and reliable decisions Enhancing trust of users in the data = Building confidence and reliance on the data Facilitating integration of data from different sources = Enabling seamless integration for comprehensive insights</p> Signup and view all the answers

Match the following outcomes with the benefits of ensuring data consistency in data modeling:

<p>Improved data analysis and reporting = Result of accurate and reliable data Reduced errors in decision-making = Outcome of consistent and error-free data Enhanced trust of users in the data = Result of consistent and trustworthy data Facilitated integration of data from different sources = Outcome of seamless integration from diverse sources</p> Signup and view all the answers

Match the following benefits with their impact on ensuring data consistency in data modeling:

<p>Improving data analysis and reporting = Enhances the quality and reliability of analysis and reporting Reducing errors in decision-making = Minimizes the risk of making incorrect decisions Enhancing trust of users in the data = Builds confidence and reliance on the data Facilitating integration of data from different sources = Enables comprehensive insights from integrated data sources</p> Signup and view all the answers

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