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Explanation: Document-Oriented Databases, such as MongoDB or Couchbase, are specifically designed to store and manage semi-structured data like JSON and XML. These databases treat data as "documents," allowing flexible schemas that can easily adapt to changes in the data structure. Unlike traditional relational databases, document databases do not require a fixed schema, making them ideal for applications where the structure of data evolves over time. Querying and indexing features in document databases are optimized for nested and hierarchical data structures commonly found in JSON and XML. Option A: Relational databases are best suited for structured data with predefined schemas, and handling semi-structured data requires additional effort (e.g., using JSON columns in PostgreSQL). Option B: Key-Value Stores are optimal for scenarios requiring fast lookups of key-value pairs but lack the hierarchical querying capabilities needed for JSON or XML data. Option C: Columnar Databases are designed for analytical workloads and structured data, making them less efficient for semi-structured formats. Option E: Flat File Storage, such as CSV files, can store data but lacks the querying and management capabilities of document-oriented databases for semi-structured formats.
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