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Anthropic CCAR-P Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Design & Architecture | 17% | - Translate business problems into Claude-based AI solutions - Design end-to-end architectures and feedback loops - Align solutions to business value pillars - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies |
| Topic 2: Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection - Implement iterative improvement pipelines - Define evaluation metrics and success criteria |
| Topic 3: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques |
| Topic 4: Governance, Safety & Risk Management | 14% | - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Manage data privacy and security compliance |
| Topic 5: Integration | 19% | - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools - Implement Model Context Protocol (MCP) integrations - Integrate with data pipelines and RAG systems |
| Topic 6: Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution - Configure Claude tools and environments for teams |
| Topic 7: Stakeholder Communication & Lifecycle Management | 14% | - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases - Manage stakeholder feedback and expectation alignment - Communicate architectural decisions and trade-offs |
Anthropic Claude Certified Architect - Professional Sample Questions:
1. An operations engineer reports that a Claude-based pipeline began returning malformed JSON responses after a scheduled maintenance window, causing downstream processing failures.
Which two investigative steps most directly isolate the root cause? (Select two.)
A) Clear the prompt cache and resubmit all pending requests to eliminate stale cached prefixes.
B) Compare the current system prompt and output schema configuration against the last known good version from before the maintenance window.
C) Increase the max_tokens limit to determine whether output truncation is causing incomplete JSON structures.
D) Replay a set of premaintenance requests against the current configuration and inspect the raw model output before downstream parsing.
E) Switch to a different Claude model tier to rule out provider-side changes as a contributing factor.
2. You are classifying chunking strategies by the corpus type each is best suited to.
For each chunking strategy, select the appropriate corpus type: "Long Structured Documents,"
"Heterogeneous Short Records," or "Code or Hierarchical Specifications."
3. You are designing a feedback session for a deployment in flight.
Which structure best supports productive stakeholder feedback?
A) Define a focused agenda, share the artifacts in advance, capture decisions and follow-ups in writing, and confirm action owners and dates.
B) Distribute artifacts at the session start rather than in advance, so stakeholders review materials in real time without preparation before the discussion begins.
C) End the session without recording decisions, follow-up items, action owners, or dates, relying on participant memory to carry the session's outcomes forward.
D) Hold an open-ended meeting with no pre-distributed agenda or artifacts, and rely on participants' memory to carry decisions and follow-up owners forward.
4. You are classifying token-management tactics by where each tactic applies in the request lifecycle: "Input Preparation," "Prompt Construction," or "Output Handling."
5. You are defining when to introduce a project subagent versus relying on Claude Code's general capabilities.
Which scenario most directly justifies a dedicated subagent?
A) The team has no recurring specialized tasks and uses Claude Code only for isolated general-purpose work that does not justify a dedicated system prompt or tool scope.
B) The team has a recurring specialized task, such as database schema review, that requires a focused system prompt, narrow tool permissions, and a specific model selection across many sessions.
C) The team wants every Claude Code interaction to use the same generic system prompt with no task- specific specialization, narrow tool permissions, or dedicated model selection.
D) The team has a one-time ad hoc question that will not recur and does not require a focused system prompt, narrow tool permissions, or dedicated model selection.
Solutions:
| Question # 1 Answer: B,D | Question # 2 Answer: Only visible for members | Question # 3 Answer: A | Question # 4 Answer: Only visible for members | Question # 5 Answer: B |

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