The correct answer is Graph data.

Graph data models entities as nodes and relationships as edges so the connections are first class and traversals across related entities are fast and natural. Graph databases are built for queries that follow relationships across many hops, which makes them ideal for scenarios like a university department hierarchy or a professional networking platform where you need to traverse from one entity to related entities.

Document data stores hierarchical or JSON like documents and it is excellent for flexible schemas and retrieving whole objects, but it is not optimized for efficient multi hop traversals across many connected entities.

Cloud Bigtable is a wide column store that excels at very large scale single row or range lookups and low latency reads and writes, but it does not provide native graph traversal capabilities and it is not the best choice for relationship heavy queries.

Relational SQL data represents relationships with foreign keys and joins and it is suitable for many structured and transactional workloads, but many hop traversals that require repeated joins become complex and can perform worse than using a purpose built graph model.

Full AWS Practitioner Certification Question