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Anthropic CCAR-P Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Claude Models, Prompting & Context Engineering13%- Context window optimization
- Claude model selection and trade-offs
- Prompt reuse and context engineering strategies
- Guardrails
- System prompts and prompt templates
Topic 2: Developer Productivity & Operational Enablement7%- AI-assisted developer workflows
- Claude tooling configuration for teams
- Debugging and operational issue resolution
- Developer enablement
Topic 3: Governance, Safety & Risk Management14%- Regulatory and compliance requirements
- Security and risk management
- AI safety and guardrails
- Human-in-the-loop validation
- Ethical AI considerations
Topic 4: Integration19%- RAG pipeline design
  • 1. Retrieval
    • 2. Indexing
      • 3. Chunking
        - Enterprise system integration
        - Authentication and authorization analysis
        - Claude integration mechanisms
        • 1. Agent-to-agent integration
          • 2. API and CLI
            • 3. MCP
              Topic 5: Evaluation, Testing & Optimization16%- A/B testing
              - Production monitoring and optimization
              - Evaluation metrics and datasets
              - Cost and performance optimization
              - System issue diagnosis
              - Evaluation framework design
              Topic 6: Solution Design & Architecture17%- Decomposition techniques for complex problem solving
              - Architectural patterns
              • 1. Workflow architectures
                • 2. Agentic architectures
                  • 3. Augmented LLM architectures
                    - Multi-agent systems and orchestration
                    - End-to-end architecture design
                    - Alignment with business value, cost, performance, and SLAs
                    - Translating business problems into Claude-based AI solutions
                    Topic 7: Stakeholder Communication & Lifecycle Management14%- Communicating architectural decisions
                    - Discovery and requirements gathering
                    - Stakeholder management
                    - Architecture documentation
                    - Solution lifecycle management
                    - Service-level agreements

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                    Anthropic Claude Certified Architect - Professional Sample Questions (Q105-Q110):

                    NEW QUESTION # 105
                    You are integrating Claude Code into the team's pull-request workflow. The team wants AI-assisted review without removing human approval.
                    Which integration design best fits this requirement?

                    Answer: C

                    Explanation:
                    Option A augments the existing review process without transferring final accountability to the model. Claude Code can inspect the change in repository context, identify logic defects, security weaknesses, regressions, and maintainability concerns, and publish structured findings for human evaluation. Existing branch protection then preserves required approvals and separation of duties. Automatic merging or disabling protection rules would convert an advisory capability into an uncontrolled authorization mechanism. Silent deletion would eliminate transparency and prevent authors from challenging or correcting the assessment.
                    Anthropic's Code Review documentation states that findings are posted as comments and do not approve or block the pull request, leaving the established review workflow intact. Claude Code Review


                    NEW QUESTION # 106
                    You are compiling continuity practices that span the deployment lifecycle.
                    Which two practices belong on the list? (Select two.)
                    Each correct answer presents a complete solution.

                    Answer: A,E


                    NEW QUESTION # 107
                    You are reviewing a peer's end-to-end design for a Claude-based platform expected to scale to thousands of concurrent users.
                    For each statement, indicate Yes if it reflects sound architectural practice. Otherwise, select No.

                    Answer:

                    Explanation:

                    Explanation:
                    * Authentication and authorization run before retrieval so retrieval can filter by identity - Yes
                    * An asynchronous queue absorbs bursty traffic between intake and the model-invocation layer - Yes
                    * Tax computation is encoded directly in the system prompt rather than in code - No
                    * Conversation logs include unredacted government identifiers to maximize tuning signal - No Authentication and authorization must precede retrieval so unauthorized information never enters model context. An asynchronous queue is appropriate for burst absorption, backpressure, controlled concurrency, and retry management at scale. Deterministic tax computation should be implemented in validated code or a controlled calculation tool, not delegated to probabilistic prompt interpretation. Including unredacted government identifiers in conversation logs violates data-minimization principles and creates unnecessary privacy, security, and regulatory exposure. Sensitive fields should be removed, tokenized, or redacted before logging or model use unless specifically required and authorized. The resulting design separates deterministic computation, access control, scalable orchestration, and language reasoning into appropriate architectural layers.


                    NEW QUESTION # 108
                    You are auditing a procurement-assistant agent whose defined responsibility is to draft purchase requests for review.
                    For each tool currently configured on the agent, select yes if the tool should remain after a least-privilege audit. Otherwise, select no if it should be removed.

                    Answer:

                    Explanation:

                    Explanation:
                    Yes, No, Yes, No, Yes
                    Least privilege requires each retained tool to be necessary for the agent's assigned responsibility and restricted to the minimum action level needed. Catalog lookup is appropriate because vendor and SKU information is needed to prepare an accurate request. The policy-document tool is similarly justified because the agent must ground the draft in applicable procurement rules. The draft-write tool is also aligned because it writes only to a review queue, preserving the required human approval boundary.
                    The inbox-send tool exceeds the stated responsibility. Sending a request directly to a vendor converts a drafting assistant into an externally acting purchasing agent and bypasses the review stage. The production- database administrator tool is unrelated to procurement and introduces unnecessary destructive capability, privilege-escalation exposure, and potential data-access risk.
                    Anthropic's prompt-injection guidance recommends limiting access to sensitive data and actions and applying least privilege so that a compromised agent can cause minimal damage. Claude Code also supports tool- specific allow, ask, and deny rules that are enforced outside the model. Prompt-Injection Mitigation, Claude Code Permissions Study Guide references/topics: Capability-bloat auditing; least privilege; tool scoping; human review gates; separation of drafting and execution authority.


                    NEW QUESTION # 109
                    You are a solution architect evaluating candidate use cases for a Claude-based program.
                    For each scenario, select Yes if Claude is appropriate as the primary solution at the architectural level.
                    Otherwise, select No.

                    Answer:

                    Explanation:

                    Explanation:
                    Drafting first-pass investigative reports from semi-structured incident logs for analyst review - Yes Computing real-time fraud scores at sub-50-millisecond latency on a streaming pipeline - No Long-context contract review with structured clause extraction and deviation flagging - Yes Replacing a vector index for semantic retrieval over a multi-million-document corpus - No Routing inbound support tickets into 30 categories with reasoning for the selected route - Yes Claude is well suited to language-intensive analysis, summarization, extraction, classification, and drafting.
                    Investigative-report drafting, contract analysis, and support-ticket routing all require interpretation of unstructured or semi-structured language and can incorporate human review. A sub-50-millisecond fraud- scoring path requires deterministic, specialized real-time processing and should not place LLM inference on the critical path. Claude also does not replace the retrieval index used to search millions of documents; it consumes the results produced by that infrastructure. The correct architectural decision separates tasks requiring semantic language reasoning from deterministic computation and information-retrieval infrastructure. Model selection and evaluation must then confirm that the chosen Claude model satisfies quality, latency, and cost requirements.


                    NEW QUESTION # 110
                    ......

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