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Anthropic CCAR-P Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Stakeholder Communication & Lifecycle Management	14%	- Discovery and requirements gathering - Solution lifecycle management - Stakeholder management - Communicating architectural decisions - Architecture documentation - Service-level agreements
Topic 2: Claude Models, Prompting & Context Engineering	13%	- System prompts and prompt templates - Guardrails - Context window optimization - Claude model selection and trade-offs - Prompt reuse and context engineering strategies
Topic 3: Solution Design & Architecture	17%	- Translating business problems into Claude-based AI solutions - Alignment with business value, cost, performance, and SLAs - Architectural patterns 1. Workflow architectures 2. Agentic architectures 3. Augmented LLM architectures - Multi-agent systems and orchestration - Decomposition techniques for complex problem solving - End-to-end architecture design
Topic 4: Integration	19%	- Enterprise system integration - RAG pipeline design 1. Retrieval 2. Chunking 3. Indexing - Claude integration mechanisms 1. MCP 2. API and CLI 3. Agent-to-agent integration - Authentication and authorization analysis
Topic 5: Developer Productivity & Operational Enablement	7%	- Claude tooling configuration for teams - Developer enablement - AI-assisted developer workflows - Debugging and operational issue resolution
Topic 6: Governance, Safety & Risk Management	14%	- Ethical AI considerations - Human-in-the-loop validation - Regulatory and compliance requirements - Security and risk management - AI safety and guardrails
Topic 7: Evaluation, Testing & Optimization	16%	- A/B testing - System issue diagnosis - Production monitoring and optimization - Cost and performance optimization - Evaluation framework design - Evaluation metrics and datasets

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Anthropic Claude Certified Architect - Professional Sample Questions (Q91-Q96):

NEW QUESTION # 91
You are selecting the documentation set that should accompany a Claude-based deployment at handoff.
Which set is most complete?

A. A single one-page architecture diagram with no ADRs, no runbooks, no on-call playbook, and no list of known limitations to support operators or future maintainers.
B. Architecture overview, ADRs, component-level diagrams, runbooks for common operations, an on-call playbook, and a list of known limitations.
C. A verbal handover walkthrough conducted on the day of transition, with no written architecture overview, ADRs, runbooks, playbook, or known limitations captured for future reference.
D. Code comments scattered across individual source files with no integrating architecture overview, no ADRs, no runbooks, and no on-call playbook to orient operators or new team members.

Answer: B

Explanation:
A complete handoff package must help different stakeholders understand, operate, troubleshoot, and evolve the Claude-based solution. An architecture overview explains the system's structure and responsibilities, while Architecture Decision Records document important decisions, alternatives, and trade-offs. Component- level diagrams provide implementation context without forcing maintainers to reconstruct dependencies from source code. Operational runbooks and an on-call playbook enable reliable incident detection, diagnosis, escalation, and recovery. Documenting known limitations is particularly important for Claude applications because model behavior can vary with prompts, context, input distribution, and external dependencies.
Options B, C, and D each provide only a partial or temporary understanding of the system. They would create knowledge gaps, increase operational risk, and make future maintenance dependent on individuals rather than durable organizational documentation.

NEW QUESTION # 92
You are transitioning a Claude-based deployment from design into implementation.
Which handoff package most directly supports a clean transition?

A. Source code alone with no integrating architecture overview, ADRs, component contracts, evaluation framework, runbooks, on-call playbook, or known-limitations register to support the delivery team.
B. A verbal walkthrough conducted on the day of handoff with no written architecture overview, ADRs, component contracts, evaluation framework, runbooks, playbook, or known limitations.
C. Architecture overview, ADRs, component contracts, evaluation framework with reference set, runbooks, on-call playbook, and known limitations.
D. The most recent set of design presentation slides without component-level diagrams, interface contracts, an evaluation framework with a reference set, runbooks, or a known-limitations register.

Answer: C

Explanation:
Option C provides the minimum coherent package required to move from architectural intent to implementable and supportable delivery. The architecture overview establishes system boundaries and component relationships. Architecture Decision Records preserve the context and trade-offs behind key choices. Component contracts define interfaces, schemas, responsibilities, failure behavior, and integration assumptions.
The evaluation framework and reference set allow the implementation team to confirm that prompts, models, retrieval components, and tool integrations continue to meet the defined quality, safety, latency, and cost thresholds. Runbooks translate operational knowledge into repeatable procedures, while the on-call playbook defines detection, escalation, containment, and recovery actions. The known-limitations register prevents unsupported behavior from being mistaken for an implementation defect and helps stakeholders manage residual risk.
Options A and B depend excessively on slides or undocumented verbal knowledge. Option D provides implementation material without explaining the system-level decisions, operating model, acceptance criteria, or limitations. Source code cannot substitute for architectural and operational documentation.
A clean transition does not mean the architect can never be consulted. It means routine implementation and support do not depend on undocumented knowledge held by a single individual.
Study Guide references/topics: Design-to-implementation handoff; ADRs; component contracts; reference evaluations; runbooks; operational playbooks; limitations management.

NEW QUESTION # 93
A Claude architect is configuring a shared Claude Code environment for a twelve-person development team.
Which two configuration decisions most directly establish a consistent, secure team environment? (Select two.)

A. Set editor theme and display preferences at the project scope so they are uniform across workstations.
B. Store the team's shared API key in the project-scope settings.json so all engineers use the same credential.
C. Apply managed configuration to enforce non-overridable security and compliance policies across all team members.
D. Instruct each engineer to configure their preferred Claude model in personal user-scope settings.
E. Commit shared MCP server definitions and tool permissions in project-scope configuration files alongside the repository.

Answer: C,E

Explanation:
Project-scope configuration is appropriate for shared MCP definitions, permissions, hooks, and other repository-specific behavior that every team member should receive consistently. Managed configuration complements this by enforcing organization-wide security and compliance requirements that individual developers cannot override. Together, these mechanisms separate collaborative project configuration from centrally controlled policy. Editor themes are personal preferences and do not materially establish a secure environment. Allowing each engineer to select an arbitrary model can undermine consistency when model choice affects behavior, cost, or approved-use requirements. Storing a shared API key in a committed project file is a serious credential-management failure. Claude Code documentation identifies .claude/settings.json as the shared project file and managed settings as the highest-precedence organizational policy layer. Claude Code: Settings files and precedence

NEW QUESTION # 94
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 # 95
You are identifying the highest-impact optimization for a deployment whose token cost is dominated by a long, repeated system prompt and a large retrieved context per request.
Which optimization most directly targets the dominant cost driver?

A. Add additional repeated content to the system prompt to give the model more guidance.
B. Switch every request to the heaviest available model to maximize output quality, accepting that higher per-request inference cost compounds rather than addresses the dominant cost driver.
C. Increase retrieval depth on every request to maximize recall, worsening the dominant cost driver by adding more retrieved tokens per request rather than reducing them.
D. Move the long, repeated system prompt into a cacheable prefix and trim retrieved context to the spans relevant to each query.

Answer: D

NEW QUESTION # 96
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