Question 2Fill in the blank: A data analytics team uses _____ to indicate consistent naming conventions for a project. This is an example of using data about data.1 pointclassificationsfolder hierarchiesversion controlmetadata
Question
Question 2Fill in the blank: A data analytics team uses _____ to indicate consistent naming conventions for a project. This is an example of using data about data.1 pointclassificationsfolder hierarchiesversion controlmetadata
Solution
A data analytics team uses _____ to indicate consistent naming conventions for a project. This is an example of using data about data.
To answer this question, we need to identify the term that refers to consistent naming conventions for a project and is an example of using data about data.
The term that fits this description is "metadata." Metadata is data that provides information about other data, including details such as the structure, format, and naming conventions of the data. In the context of a data analytics team, metadata can be used to ensure consistent naming conventions for a project, making it easier to organize and analyze the data effectively.
Similar Questions
Question 1A data analytics team labels its files to indicate their content, creation date, and version number. The team is using what data organization tool?1 pointFile-naming attributesFile-naming verificationsFile-naming conventions File-naming references
Question 3A data analyst team revisits an old project and wants to understand how the file-naming conventions are structured. Where does the team locate this information?1 pointIn folder hierarchiesIn SQLIn the metadataIn aggregated data
Question 2To align file naming and storage practices, it’s useful to develop metadata practices with your data analytics team.1 pointTrueFalse
A data analyst uses _____ to organize multiple files for a given project so they can be found and accessed in an efficient manner.1 pointdata groupingfolderingversion controldata hygiene
Question 5Fill in the blank: During an analysis project, _____ might involve merging or splitting datasets in order to prepare them for analysis.1 pointformatting and adjusting dataorganizing datagetting input from otherstransforming data
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