An analytics team receives hourly files that contain millions of user-interaction events. Each file is a UTF-8 text document where every event is stored as a single JSON object that may include nested arrays (for example, "events" and "line_items") and embedded objects (such as "device" or "shippingAddress"). Before choosing a storage platform, the lead analyst must label the data structure according to CompTIA Data+ categories. How should the events file be classified, and what attribute of the file best justifies that choice?
Unstructured data, because the contents are free-form text without any inherent delimiters or metadata.
Semi-structured data, because the JSON objects are self-describing key/value pairs that can vary in depth and attribute set.
Structured data, because the file can be split into rows and columns with a fixed schema once it is loaded into a relational database.
Binary large object data, because the entire file should be treated as a single opaque blob until it is decoded.
The file should be classified as semi-structured data because JSON provides self-describing key/value pairs and hierarchical tags that give the data an intrinsic, machine-readable structure-yet the exact set or order of attributes can differ from one record to another. This places it between fully structured relational tables (which require a rigid, predefined schema) and unstructured text or binary files (which contain no inherent metadata). The flexibility and presence of nested objects are the decisive characteristics. The other options are incorrect because: a relational table would lose the variable depth and optional fields found in JSON; treating the content as unstructured ignores the explicit keys and nesting that clearly describe each element; and calling the file a BLOB conflates the storage mechanism with the data structure and would discard the metadata that makes JSON analyzable without external schema documentation.
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CompTIA Data+ DA0-002 (V2)
Data Concepts and Environments
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