Reference · Exam Blueprint

Claude Certified Architect – Foundations

Source: official exam guide PDF, v0.1. Compressed for quick review — go back to the PDF for full "knowledge of / skills in" detail on any task statement.

The exam itself

The recurring distractor pattern
Across all 12 official sample questions, wrong answers cluster into the same shapes: relying on the model's probabilistic compliance (prompt wording, few-shot examples, self-reported confidence) when the situation demands a deterministic/programmatic guarantee — or the reverse, over-engineering infrastructure (custom classifiers, routing layers) when a small prompt/description fix is the proportionate first step. Learn to ask: "does this need a guarantee, or just better guidance?"

Domain weights

1. Agentic Architecture & Orchestration 27%
3. Claude Code Configuration & Workflows 20%
4. Prompt Engineering & Structured Output 20%
2. Tool Design & MCP Integration 18%
5. Context Management & Reliability 15%

Domain 1 — Agentic Architecture & Orchestration (27%)

TaskOne-line summary
1.1Agentic loop: check stop_reason (tool_use vs end_turn), append tool results, loop. Never parse text or use iteration caps as the stopping condition.
1.2Hub-and-spoke: coordinator routes all inter-subagent comms; subagents don't share memory automatically.
1.3Task tool spawns subagents (needs allowedTools: ["Task"]); pass context explicitly; parallel = multiple Task calls in one turn.
1.4Programmatic prerequisites (hooks/gates) for deterministic ordering; prompts alone have non-zero failure rate.
1.5Hooks (PostToolUse, call interception) for guaranteed compliance — normalize data, block policy violations.
1.6Prompt chaining (fixed sequential) vs. dynamic decomposition (adaptive) — pick based on predictability of the task.
1.7Session mgmt: --resume <name> to continue; fork_session to branch; fresh session + summary beats resuming stale tool results.

Domain 2 — Tool Design & MCP Integration (18%)

TaskOne-line summary
2.1Tool descriptions are THE tool-selection mechanism. Minimal/overlapping descriptions → misrouting. Fix descriptions before adding infrastructure.
2.2Structured MCP errors: isError, errorCategory (transient/validation/permission/business), isRetryable. Generic "failed" messages block recovery.
2.3Fewer tools per agent (4–5, not 18) improves selection reliability. Scope tools to role. tool_choice: auto / any / forced.
2.4.mcp.json (project, shared) vs ~/.claude.json (personal). Env-var expansion for secrets. MCP resources expose catalogs to cut exploratory calls.
2.5Grep = content search, Glob = path patterns, Edit needs unique anchor text (fallback: Read+Write).

Domain 3 — Claude Code Configuration & Workflows (20%)

TaskOne-line summary
3.1CLAUDE.md hierarchy: user (~/.claude, not shared) → project (root/.claude, shared via VCS) → directory. @import for modularity.
3.2Commands: .claude/commands/ (project) vs ~/.claude/commands/ (personal). Skills: context: fork isolates output, allowed-tools restricts, argument-hint prompts for args.
3.3.claude/rules/ with YAML paths: glob-scoping beats directory-level CLAUDE.md when conventions cut across directories (e.g. all *.test.tsx).
3.4Plan mode = architectural/multi-file/ambiguous scope. Direct execution = single-file, well-understood change.
3.5Concrete I/O examples, test-first iteration, "interview pattern," batch interacting issues vs. sequential independent ones.
3.6-p/--print for non-interactive CI. --output-format json + --json-schema for machine-parseable findings. Independent review instance > self-review.

Domain 4 — Prompt Engineering & Structured Output (20%)

TaskOne-line summary
4.1Explicit categorical criteria beat vague instructions ("be conservative") for precision/false-positive reduction.
4.2Few-shot examples: best tool for consistent format + generalizing judgment to ambiguous/novel cases.
4.3tool_use + JSON schema eliminates syntax errors (not semantic ones). Nullable fields prevent fabrication. tool_choice forces a specific tool first.
4.4Retry-with-error-feedback works for format errors; doesn't work when info is simply absent from source.
4.5Message Batches API: 50% cheaper, ≤24h window, no SLA, no mid-request tool calls. Use for non-blocking workloads only; custom_id correlates.
4.6Independent review instance (no generator's reasoning context) beats self-review. Split large reviews: per-file pass + cross-file integration pass.

Domain 5 — Context Management & Reliability (15%)

TaskOne-line summary
5.1"Lost in the middle": keep a persistent "case facts" block; trim verbose tool output to relevant fields; put key summaries first/last.
5.2Escalate on: explicit customer request, policy gap/exception, inability to progress, multiple ambiguous matches. NOT on sentiment or self-reported confidence.
5.3Structured error context (failure type, attempted query, partial results) → coordinator recovery. Never silently suppress or hard-terminate on one failure.
5.4Scratchpad files + subagent delegation counteract context degradation in long exploration. /compact when verbose. Manifests for crash recovery.
5.5Aggregate accuracy hides per-segment failure. Stratified sampling + field-level confidence calibrated on labeled data, not vibes.
5.6Preserve claim→source mappings through synthesis. Annotate conflicts (don't arbitrarily pick one). Require dates for temporal data.

The 6 exam scenarios (4 of these appear)

  1. Customer Support Resolution Agent — MCP tools (get_customer, lookup_order, process_refund, escalate_to_human), 80%+ first-contact resolution target. Domains 1, 2, 5.
  2. Code Generation with Claude Code — slash commands, CLAUDE.md, plan mode vs direct execution. Domains 3, 5.
  3. Multi-Agent Research System — coordinator + web-search/document-analysis/synthesis/report subagents. Domains 1, 2, 5.
  4. Developer Productivity with Claude — built-in tools + MCP servers for codebase exploration. Domains 2, 3, 1.
  5. Claude Code for CI — automated review/test-gen in CI/CD, minimizing false positives. Domains 3, 4.
  6. Structured Data Extraction — JSON-schema validated extraction from unstructured docs. Domains 4, 5.

Explicitly out of scope

Fine-tuning · API auth/billing/rate limits · specific programming language/framework internals · MCP server hosting/infra · Claude's internal architecture/training/RLHF · embeddings/vector DBs · computer use · vision · streaming API internals · prompt caching internals · tokenization algorithms · cloud provider (AWS/GCP/Azure) specifics · performance benchmarking/model comparison metrics.

Appendix: technologies & concepts checklist

Straight from the official guide's own appendix — if a term here doesn't ring a bell, that's a gap to close before exam day.

AreaWhat to know
Claude Agent SDKAgent definitions, agentic loops, stop_reason handling, hooks (PostToolUse, tool call interception), subagent spawning via Task, allowedTools
MCPServers, tools, resources, isError flag, tool descriptions, tool distribution, .mcp.json, environment variable expansion
Claude CodeCLAUDE.md hierarchy, .claude/rules/, .claude/commands/, .claude/skills/ (context: fork, allowed-tools, argument-hint), plan mode, direct execution, /memory, /compact, --resume, fork_session, Explore subagent
Claude Code CLI-p/--print, --output-format json, --json-schema
Claude APItool_use with JSON schemas, tool_choice (auto/any/forced), stop_reason values, max_tokens, system prompts
Message Batches API50% cost savings, 24h window, custom_id correlation, polling, no multi-turn tool calling
JSON Schema / PydanticRequired vs. optional, enums, nullable fields, "other"+detail pattern, strict mode, semantic vs. syntax validation
Built-in toolsRead, Write, Edit, Bash, Grep, Glob — purpose and selection criteria for each
PromptingFew-shot for ambiguous scenarios and format consistency, prompt chaining, explicit criteria
Context & reliabilityToken budgets, progressive summarization, lost-in-the-middle, scratchpad files, session isolation, confidence calibration, stratified sampling

8 exam preparation recommendations (official)

  1. Build an agent with the Claude Agent SDK — a complete loop with tool calling, error handling, session management, subagent spawning.
  2. Configure Claude Code for a real project — CLAUDE.md hierarchy, .claude/rules/, a custom skill, at least one MCP server.
  3. Design and test MCP tools — differentiated descriptions, structured errors, tool-selection reliability under ambiguity.
  4. Build a structured data extraction pipeline — tool_use + JSON schema, validation-retry, optional/nullable fields, batch processing.
  5. Practice prompt engineering — few-shot for ambiguity, explicit review criteria, multi-pass review architectures.
  6. Study context management patterns — structured fact extraction, scratchpad files, subagent delegation for context limits.
  7. Review escalation and human-in-the-loop patterns — when to escalate vs. resolve autonomously, confidence-based review routing.
  8. Complete the official practice exam before sitting the real one (link provided separately by Anthropic — see RESOURCES.md for status).

Recommendations 1–4 map directly to Preparation Exercises 1–4. Recommendations 5–7 are covered across Lessons 10–14. Recommendation 8 is tracked as an open resource gap.