A requirements workshop for a field-inspection solution produced three items: inspectors capture results on tablets with no connectivity at remote sites, roughly 200,000 product rows arrive from an ERP system every night, and each failed inspection needs a two-stage manager approval with notifications. Which mapping of requirement to component type is the most appropriate design?

Decomposing requirements by implementation approach is the first architecture step: user interaction belongs in an app, bulk system-to-system movement belongs in server-side integration code, and human-in-the-loop process steps belong in automation. A Power Apps app with a mobile offline profile is the supported way to let users work against Dataverse rows without connectivity and synchronize later. A nightly load of hundreds of thousands of rows fits Azure-hosted code that batches writes with bulk messages such as CreateMultiple and UpdateMultiple and honors service protection limits. A cloud flow with the Approvals connector provides staged approvals with Teams and Outlook notifications. Caching rows in a web resource is not a supported offline mechanism, and a synchronous plug-in runs inside a single Dataverse operation, so it cannot pull an external feed. A chat agent is not an offline data-capture surface, a per-row flow multiplies runs and throttling, and business rules cannot route approvals. A hand-built local queue in a code app reinvents synchronization, and client script cannot run an approval that outlives the form. Exam caveat: expect the answer that matches each requirement to its native component type. Operational check: list every requirement with its trigger, volume and actor, then confirm each has exactly one owning component.