[03/Tennsift/01]

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<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

<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

<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

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

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

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

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

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

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

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

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>True</p> Signup and view all the answers

<p>False</p> Signup and view all the answers

<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

<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

<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

<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

<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

<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

<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