Domain 1 – Solution Design & Architecture (17%)

6 lessons, each ending with a practice scenario

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1. ✓1.1 — Translating Business Problems into Claude SolutionsTranslating a business problem into a Claude solution produces a measurable success criterion, a bounded scope, and a defensible boundary between the model and ordinary software.
2. ✓1.2 — End-to-End Architectures and Feedback LoopsEvery production Claude system has six stages: Input — ingestion, validation, normalisation, PII handling (decide before model); Context assembly — retrieval, memory, prompt construction; Processing — model call(s): single, workflow, or agent loop; Output — schema validation, business-rule checks, guardrail screening, designed failure path. Model output is untrusted until validated.; Delivery — sync, async, event, or draft-for-review (risk decision); Feedback loops — user feedback, automated eval on sampled traffic, monitoring → iteration.
3. ✓1.3 — Selecting Architectural PatternsFind the simplest solution that meets requirements; add complexity only when it demonstrably improves outcomes. Escalate tier only when the simpler tier cannot meet a named requirement.
4. ✓1.4 — Multi-Agent Systems and OrchestrationExcels at breadth-first work that parallelises cleanly: research across independent directions, analysis of many independent units, tasks exceeding one context window, work needing distinct specialisms/tools/permissions.
5. ✓1.5 — Decomposition Techniques for Complex ProblemsOne pass covering too much → inconsistent depth, missed items, contradictory judgements. Fix (both mandatory):; Per-unit local passes (consistent depth on every unit); Separate integration pass (cross-unit issues invisible to local passes).
6. ✓1.6 — Aligning Solutions to Business Value PillarsTest: does the capability already exist? Yes → efficiency. No → transformation. Design Decisions Tied to Pillars: Model selection — per-task cost/performance; smallest model that meets quality bar; Prompt caching — ~90% cache-read cost reduction; serves cost + latency SLA (static first, dynamic last); Batch processing — halves cost of async work; sacrifices request-path latency; Multi-agent — ~15× tokens; name the pillar that pays for it.