Structured data requires a predefined schema while semi-structured data contains self-describing fields and flexible structure.

This option is correct because structured data is defined by a fixed schema that is enforced when data is written and validated by systems like relational databases and data warehouses. Semi-structured data uses self describing formats such as JSON or XML so records can vary and the schema can be applied when the data is read.

Semi structured formats carry field names or tags with the data which makes them flexible for evolving sources and for ingesting varied records without changing a central schema. This flexibility is why teams often use schema on read for semi structured data and schema on write for structured data.

Structured data is stored exclusively in relational databases and semi-structured data is only saved in object storage is incorrect because storage is not exclusive to a format. Structured data can live in warehouses, columnar stores, or specialized databases and semi structured data can be stored in databases, document stores, or object storage depending on access and tooling.

Semi-structured data is easier to scale for large analytics workloads compared with structured data is incorrect because scalability depends on the chosen storage and processing architecture rather than the inherent format. Both structured and semi structured data can scale well when used with distributed systems and the right data engineering patterns.

Structured data is always more secure than semi-structured data is incorrect because security is determined by access controls, encryption, and governance policies rather than the data format. Both formats can be protected to strong security standards when proper controls are applied.

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