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Anthropic CCAR-P Exam Syllabus Topics:

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

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                    Anthropic Claude Certified Architect - Professional Sample Questions (Q19-Q24):

                    NEW QUESTION # 19
                    You are running a discovery session with a business sponsor who asks for "an AI system to fix our customer escalations." Which approach best yields actionable requirements?

                    Answer: D

                    Explanation:
                    The sponsor's statement expresses a desired outcome but does not define an implementable requirement.
                    Option B converts the vague request into concrete information about users, workflow triggers, expected resolutions, decision boundaries, and non-functional constraints. These findings can then be translated into measurable success criteria, evaluation cases, integration requirements, and escalation rules. Anthropic's evaluation guidance states that an LLM application should begin with specific, measurable, achievable, and relevant success criteria. Option A prematurely treats an aspiration as a complete specification. Option C incorrectly transfers discovery responsibility entirely to the sponsor, while Option D imports assumptions from an unrelated context. Effective Claude architecture requires traceability from the business problem through requirements, evaluation measures, design decisions, and production acceptance criteria. Anthropic:
                    Define success criteria and build evaluations


                    NEW QUESTION # 20
                    You are designing a test strategy for a Claude-based pipeline that handles sensitive financial data.
                    Which two test types should be prioritized to cover both safety under attack and cross-component correctness? (Select two.)

                    Answer: A,C

                    Explanation:
                    Adversarial tests directly evaluate behavior under attack, including prompt injection, malformed records, malicious document content, credential-extraction attempts, and unauthorized tool requests. Integration tests provide the complementary coverage required by the question: they verify authentication, retrieval, prompt construction, model invocation, validation, logging, and downstream actions as one end-to-end system.
                    Regression, smoke, and unit testing remain valuable, but they do not jointly satisfy the two stated objectives.
                    A stable regression set measures previously established behavior; smoke tests provide shallow availability confirmation; and prompt-rendering unit tests cover only one component. Anthropic recommends explicit prompt-injection defenses and task-specific evaluations that represent both normal and edge-case traffic.
                    Prompt-injection mitigation


                    NEW QUESTION # 21
                    You are sequencing decomposed components in an invoice-processing pipeline.
                    For each of the decomposed components, select the execution layer it belongs to: "Pre-Processing," "Model Stage," or "Post-Processing."

                    Answer:

                    Explanation:

                    Explanation:
                    Model Stage, Post-Processing, Post-Processing, Model Stage, Pre-Processing, Pre-Processing Pre-processing prepares source material before Claude receives it. Optical character recognition converts scanned invoice images into machine-readable text. Because PII redaction is explicitly performed before model invocation, it also belongs in pre-processing. Additional activities at this layer can include file validation, malware scanning, normalization, page separation, and metadata extraction.
                    The model stage contains tasks requiring Claude's language-understanding capability. Classifying the invoice type involves interpreting textual and contextual features, while extracting structured invoice fields requires mapping unstructured content into defined business attributes.
                    Post-processing verifies and commits the model's result. Schema validation confirms that required fields exist, data types are correct, enumerated values are permitted, and structural constraints are satisfied.
                    Persistence must occur only after validation and any required human review because the system of record should not receive malformed or unapproved model output.
                    This decomposition separates probabilistic inference from deterministic processing. OCR, redaction, validation, and persistence do not need to be delegated to Claude when conventional components can execute them more predictably. The resulting architecture reduces model workload, strengthens privacy controls, improves auditability, and prevents unvalidated output from directly changing authoritative records.
                    Study Guide references/topics: Task decomposition; pre-processing; model inference; post-processing; deterministic validation; privacy-by-design; system-of-record protection.


                    NEW QUESTION # 22
                    You are integrating Claude Code into a workflow that runs against a production database.
                    Which guardrail design most directly preserves safety on data-modifying operations?

                    Answer: C

                    Explanation:
                    Option D applies three complementary controls. First, a read-only database credential creates an authorization boundary outside the model; prompt instructions alone cannot convert that credential into write access.
                    Second, restricting the subagent's tool list reduces capability exposure by preventing the agent from selecting unrelated or unnecessarily privileged operations. Third, explicit human confirmation creates a deliberate approval gate before any exceptional data-changing action is executed.
                    This is defense in depth. If Claude misinterprets a request or processes malicious instructions from untrusted content, the restricted credential and tool configuration limit the available action surface. Human review then protects operations with potentially irreversible production consequences. Logging and audit trails should remain enabled to record the actor, request, tool call, approval, affected records, and outcome.
                    Anthropic documents that Claude Code begins with read-only permissions in Manual mode and requests approval for actions that modify the environment. Its permission system also supports granular allow, ask, and deny controls for MCP tools and subagents. Claude Code Security , Configure Permissions Options A and B eliminate least privilege and approval boundaries. Option C removes essential detection and forensic evidence.
                    Study Guide references/topics: Production database safety; least privilege; read-only defaults; MCP permissions; subagent scoping; human confirmation; auditability.


                    NEW QUESTION # 23
                    You are building a feedback-and-alignment routine for a multi-stakeholder deployment.
                    Which two practices belong in the routine? (Select two.)
                    Each correct answer presents a complete solution.

                    Answer: B,C

                    Explanation:
                    Alignment is an ongoing governance process rather than a one-time approval. Option D creates a predictable cadence for reassessing objectives, constraints, assumptions, risks, service levels, and success measures as business and technical conditions change. Option E requires disagreements to be surfaced and reconciled explicitly, preserving decision traceability and preventing unresolved conflicts from reappearing during implementation or operation. The routine should document decisions, owners, accepted trade-offs, action items, and conditions that trigger reconsideration. Recording meetings may improve accessibility but does not itself produce alignment. Rotating facilitation is optional and may weaken accountability. Automatically escalating every disagreement to an executive sponsor bypasses normal architectural negotiation and should be reserved for material deadlocks.
                    Study Guide references/topics: Stakeholder alignment; feedback cadence; assumption management; conflict reconciliation; decision records; lifecycle governance.


                    NEW QUESTION # 24
                    ......

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