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Anthropic CCAR-P Exam Syllabus Topics:

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

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Anthropic Claude Certified Architect - Professional CCAR-P Prüfungsfragen mit Lösungen (Q84-Q89):

84. Frage
You are identifying inefficiency in a Claude Code workflow where each engineer manually re-explains the project's conventions and architecture in every session.
Which adjustment most directly removes this inefficiency?

A. Capture the project's conventions and architecture in a project-scoped CLAUDE.md (or equivalent persistent project-context file) committed to the repository, so each session loads it automatically.
B. Tell each engineer to retype the project conventions and architecture context more quickly at the start of each session, reducing time lost without eliminating the repeated manual effort.
C. Remove all documented project conventions and architectural standards so engineers have nothing to re- explain at session start, accepting the loss of consistency and shared coding standards.
D. Forbid the use of Claude Code on the project entirely to avoid the session-initialization overhead, accepting that the team loses all AI-assisted development productivity for this codebase.

Antwort: A

Begründung:
A project-scoped CLAUDE.md converts repeatedly supplied information into persistent, version-controlled context. Claude Code loads applicable CLAUDE.md instructions at session initialization, allowing architecture principles, coding conventions, build commands, repository structure, and verification requirements to be shared consistently across engineers. Anthropic explicitly recommends using CLAUDE.
md for information that developers would otherwise explain repeatedly; concise and specific instructions provide stronger adherence. Committing the file also enables normal review and change-control practices.
Option C reduces typing speed but preserves the underlying waste and inconsistency. Options A and D remove useful capability or governance rather than correcting the workflow. The implementation should periodically review the file for obsolete, conflicting, or unnecessarily verbose instructions.
Study Guide references/topics: Claude Code project memory ; CLAUDE.md; persistent context; repository conventions; workflow standardization.

85. Frage
You are integrating AI-assisted tooling into the team's documentation workflow. The team wants generated documentation that stays grounded in the actual code.
Which integration approach best fits this requirement?

A. Configure subagents that read the relevant code files via filesystem and code-search tools, generate the documentation, and emit changes through the team's normal review workflow.
B. Disable all filesystem and code-search tools so the subagents cannot read any repository code, accepting that documentation generation will be entirely disconnected from the actual implementation.
C. Generate documentation from the model's training-data recall without reading any of the actual repository code, accepting that the output will not reflect the current implementation.
D. Have the subagents publish generated documentation directly to the public-facing site without passing through the team's normal review workflow or any human approval step.

Antwort: A

Begründung:
Documentation remains grounded only when the generating agent can inspect the authoritative implementation. A purpose-built subagent should receive scoped filesystem and code-search capabilities, identify the relevant modules, interfaces, configuration files, tests, and call sites, and generate documentation from that evidence. This approach minimizes reliance on training-data recall, which cannot represent the repository's current private code or recent changes.
Subagents are useful because repository exploration occurs in their own context, allowing them to return focused findings or proposed edits without filling the main conversation with every file read. Their tool access should follow least privilege: read access to required paths, narrowly controlled write access, and explicit exclusions for secrets or unrelated repositories.
Generated documentation should enter the team's established change-control process as a patch, branch, or pull request. Normal review, automated checks, link validation, example execution, and human approval provide accountability and prevent unverified material from being published. Direct publication bypasses these safeguards, while disabling repository tools makes factual grounding impossible.
Study Guide references/topics: [Claude Code custom subagents](https://docs.anthropic.com/en/docs/claude- code/sub-agents); [codebase exploration workflows] (https://docs.anthropic.com/en/docs/claude-code
/common-workflows); source-grounded generation; least-privilege tools; human review and lifecycle controls.

86. Frage
You are evaluating prompting claims in a peer's design document.
For each claim, select yes if the claim reflects sound practice. Otherwise, select no.

Antwort:

Begründung:

Explanation:
Yes, Yes, No, Yes, No
Zero-shot prompting is a reasonable baseline for a clearly defined closed-set classifier because the model receives both the permissible labels and their meanings. Performance should then be measured against an evaluation set before additional prompt complexity is introduced. Few-shot examples are also sound for extraction because examples containing the exact field names demonstrate the required schema and reduce formatting ambiguity. Anthropic identifies examples as one of the most reliable methods for steering output format, structure, and consistency. Prompting Best Practices Chain-of-thought is not an unconditional default. Additional reasoning is appropriate when the task requires substantive decomposition or complex judgment, but routine classification and extraction may gain little while consuming more tokens and increasing latency. The technique should be selected according to evaluated task requirements.
An even-handed comparison governed by explicit criteria mitigates leading-question bias by requiring symmetrical treatment of alternatives rather than presupposing a preferred conclusion. Finally, prompt validation is model-specific. New model versions may follow instructions differently, change formatting behavior, alter tool selection, or produce different latency and cost characteristics. Anthropic's migration guidance consequently recommends reevaluating prompts and workloads against the new baseline. Model Migration Guide Study Guide references/topics: Zero-shot prompting; few-shot examples; reasoning techniques; neutral framing; model migration and regression evaluation.

87. Frage
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?

A. Accept the sponsor's original phrasing as a complete and final requirement and proceed directly to design without asking targeted questions to surface actors, triggers, or constraints.
B. Decompose the request by asking targeted questions about who escalates, what triggers escalation, what good resolution looks like, and which constraints-latency, cost, audit, and data sensitivity-apply.
C. Defer the discovery session indefinitely on the grounds that the sponsor must fully specify the system before the architect can ask any clarifying or scoping questions.
D. Assume the requirement matches the architect's previous engagement in a different industry and proceed with that context, without validating actors, triggers, or constraints for this sponsor.

Antwort: B

Begründung:
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

88. Frage
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.)

A. Adversarial tests using prompt-injection and malformed-input cases.
B. Smoke tests that verify core paths after each deployment.
C. Regression tests against a stable reference set of previously known-good outputs.
D. Integration tests that verify end-to-end pipeline behavior across all components.
E. Unit tests targeting only individual prompt-template rendering logic.

Antwort: A,D

Begründung:
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

89. Frage
......

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