Describe three data exchange formats and their typical use cases in CDX workflows.

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Multiple Choice

Describe three data exchange formats and their typical use cases in CDX workflows.

Explanation:
YAML is a natural fit for describing APIs and their interfaces because it is human-readable and widely used in API specifications (like OpenAPI). This makes it easy to outline endpoints, request and response schemas, and metadata in a single, editable document, which helps teams design, validate, and share API contracts in CDX workflows. JSON works well for databases because it is lightweight, easy to parse across programming languages, and maps cleanly to the structured data many databases store or exchange. It supports flexible schemas and is commonly used for data interchange with both document stores and relational systems when feeding data into or pulling data from a database. TXT (plain text) serves as a simple, unstructured container for straightforward or minimally encoded payloads. When a lightweight, text-based transfer is sufficient—such as small binary-like payloads represented in a readable form or logs and simple transcripts—TXT can be convenient in CDX workflows. Other pairings don’t align as neatly with common practice: JSON for binary data tends to complicate encoding, XML for images is inefficient for typical image transfer, and CSV doesn’t capture nested structures well, which limits its usefulness for modern data interchange.

YAML is a natural fit for describing APIs and their interfaces because it is human-readable and widely used in API specifications (like OpenAPI). This makes it easy to outline endpoints, request and response schemas, and metadata in a single, editable document, which helps teams design, validate, and share API contracts in CDX workflows.

JSON works well for databases because it is lightweight, easy to parse across programming languages, and maps cleanly to the structured data many databases store or exchange. It supports flexible schemas and is commonly used for data interchange with both document stores and relational systems when feeding data into or pulling data from a database.

TXT (plain text) serves as a simple, unstructured container for straightforward or minimally encoded payloads. When a lightweight, text-based transfer is sufficient—such as small binary-like payloads represented in a readable form or logs and simple transcripts—TXT can be convenient in CDX workflows.

Other pairings don’t align as neatly with common practice: JSON for binary data tends to complicate encoding, XML for images is inefficient for typical image transfer, and CSV doesn’t capture nested structures well, which limits its usefulness for modern data interchange.

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