Anthropic CCAR-P - Claude Certified Architect - Professional

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.

The request is not yet design-ready because it lacks a defined failure, affected user journey, supported decision, target population, and measurable outcome. The architect should therefore facilitate structured discovery rather than prematurely selecting an AI pattern or model.

Discovery should identify where the current customer journey breaks down, who experiences the problem, what decision or task requires assistance, what data is available, and which metric would demonstrate improvement. The team can then establish baseline performance, target thresholds, operational constraints, risks, and explicit non-goals. Anthropic’s evaluation guidance states that an LLM application should begin with clearly defined success criteria and a method for measuring them. Define Success Criteria and Build Evaluations

Option A abandons the sponsor instead of helping translate a business concern into an actionable problem. Option B risks producing a generic demonstration unrelated to a validated customer need. Option C substitutes the architect’s assumptions for stakeholder evidence and may commit the organization to the wrong scope.

Competent delegation begins by defining the decision, intended outcome, authority boundaries, and target metric before determining which responsibilities should be assigned to Claude, conventional software, or human reviewers.

Study Guide references/topics: Structured discovery; delegation competency; problem definition; target metrics; decision support; measurable business outcomes.

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Role-based access control directly supports HIPAA’s access-control and minimum-necessary principles. The retrieval and orchestration layers must verify the authenticated user’s role before protected health information enters the model context. Authorization should be applied to individual records, data sources, tools, and actions, with audit events recording the requesting identity, accessed resource, purpose, and result. Model instructions alone are not a security boundary.

Patient information must also remain within services and processing activities covered by the organization’s Business Associate Agreement. Anthropic states that PHI processing requires a HIPAA-ready organization and an accepted BAA, and that only specified eligible services and configurations are covered. Patient data must not be submitted through training, feedback, beta, third-party, or integration pathways that fall outside the applicable agreement. A BAA is not blanket authorization; architects must verify feature eligibility and disable inappropriate data-sharing mechanisms.

Reducing max_tokens, selecting a more capable model, and enabling streaming affect output length, quality, or latency. They do not establish authorization, contractual coverage, minimum-necessary access, or compliant PHI handling.

Study Guide references/topics: Anthropic Business Associate Agreement requirements ; HIPAA-ready services; PHI access control; minimum necessary; eligible-service boundaries; auditability.

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The interaction model and portability requirement are architectural decision drivers. MCP is appropriate when tools or data sources must be exposed through a standardized interface reusable across compatible AI clients. Direct API integration is often preferable for a narrow, stateless, latency-sensitive call where the application already owns the integration logic. Agent-to-agent handoff is better suited to stateful or longer-running delegation between independently responsible agents. Documentation quality and team familiarity influence implementation effort but do not determine the correct integration pattern. Encryption in transit is a mandatory security property for production integration rather than a differentiating architectural requirement; each viable approach must provide it. Anthropic distinguishes direct Messages access from stateful managed-agent infrastructure, while MCP supplies a standardized tool integration layer. Claude Platform documentation

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.

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The evaluation metrics must map directly to the two stated requirements. Role-based access-control enforcement rate, tested against a red-team dataset, measures whether requests from unauthorized identities, roles, or access paths are consistently denied. It converts the privacy and authorization requirement into an observable security metric and exposes bypass conditions that ordinary functional tests may miss. Because the requirement says patient data must never reach unauthorized roles, the acceptance threshold should be effectively 100% enforcement across representative and adversarial cases.

Per-query token cost directly measures compliance with the financial constraint. The metric should include uncached input, cache reads and writes where applicable, output tokens, thinking tokens, and any other billable components attributed to the request. Results can then be compared with the defined ceiling across normal, boundary, and worst-case inputs.

Anthropic’s evaluation guidance identifies privacy preservation and price as distinct success criteria and emphasizes that criteria must be specific, measurable, and relevant to the application. Define Success Criteria and Build Evaluations

BLEU may assess textual similarity but does not test authorization or cost. Throughput and percentile latency are valuable operational metrics, yet neither directly answers the two requirements in the scenario.

Study Guide references/topics: Multidimensional evaluation; privacy preservation; authorization testing; red-team datasets; token-cost measurement; requirement-to-metric traceability.

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AI-generated code requires verification through the same controlled engineering workflow as human-authored changes. Removing review eliminates the gate that can detect incorrect assumptions, security defects, missing tests, and unintended behavior. Line count is not a defensible risk measure: a one-line authorization change, dependency alteration, or command-execution defect can be more consequential than a large documentation update. Review requirements should instead be determined by affected components, privilege, data sensitivity, reversibility, test coverage, and deployment impact. Expanding capacity may be operationally useful but is not the central diligence objection. Raising the threshold retains the same flawed proxy, while rejecting all AI-generated changes discards legitimate productivity benefits. Anthropic states that users remain responsible for reviewing proposed code and commands. Claude Code security

The prompt contains mutually incompatible behavioral requirements. The architect should resolve the contradiction explicitly so the model receives one coherent grounding policy. If the application requires source-supported answers, the prompt should prohibit unsupported completion, permit an insufficient-evidence response, and require citations or supporting passages. Adding another priority instruction leaves the underlying conflict in place and creates ambiguity about when speculation is permitted. Increasing temperature makes behavior more variable rather than more correct. Retaining both instructions produces inconsistent outputs across requests and model versions. Anthropic recommends clear, direct, specific prompting and notes that conflicting project instructions can cause Claude to select among them unpredictably. Prompting best practices ; project-instruction troubleshooting

Project-scoped configuration is the appropriate mechanism for repository-specific settings that must remain consistent across collaborators. Claude Code recognizes `.claude/settings.json` as the shared project settings file and `.mcp.json` as the project-level MCP server configuration. When these files are committed to source control, every engineer receives the same definitions through the repository’s normal clone, pull, review, and versioning workflow.

Anthropic’s scope hierarchy distinguishes project configuration from user and local configuration. Files under the user scope apply to one engineer and are not automatically shared. Operating-system environment variables are also workstation-specific and require separate configuration management. Managed settings can be distributed centrally, but they are primarily intended for organization-wide security policies and controls that must be enforced by administrators; they are less appropriate when the configuration belongs specifically to one repository.

Shared configuration should contain server definitions and environment-variable references, but not plaintext credentials. Each engineer can resolve required secrets through an approved local secret mechanism while retaining identical server names, transports, arguments, and non-sensitive settings. Project MCP servers also remain subject to workspace trust and approval controls before execution.

Study Guide references/topics: [Claude Code configuration scopes](https://docs.anthropic.com/en/docs/claude-code/settings); project-scoped MCP servers; `.mcp.json`; repository-based operational standardization; secure secret separation.

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Function-level or section-level chunking for code modules — Code or Hierarchical Specifications

Tree-aware chunking that follows code or specification hierarchy — Code or Hierarchical Specifications

Per-record chunking where each record is one chunk — Heterogeneous Short Records

Semantic chunking along clause or paragraph boundaries — Long Structured Documents

Fixed-size chunking with overlap for short records of similar length — Heterogeneous Short Records

Hierarchical chunking that mirrors document section structure — Long Structured Documents

Chunking must preserve the structural unit that carries meaning in the source corpus. Code and hierarchical specifications are best divided at function, module, class, or tree boundaries because arbitrary token cuts can separate definitions from their implementation or parent context. Heterogeneous short-record collections should generally preserve each record as an independent chunk. Fixed-size overlapping chunks are also effective when records have broadly similar lengths and lack meaningful internal hierarchy. Long structured documents benefit from semantic boundaries such as clauses and paragraphs, while hierarchical chunking preserves relationships among sections, subsections, and parent headings. Anthropic notes that chunk size, boundaries, and overlap materially affect retrieval performance; therefore, one universal chunking method is inappropriate. Anthropic Contextual Retrieval