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| Section | Weight | Objectives |
|---|---|---|
| Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools |
| Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs |
| Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions - Design end-to-end architectures and feedback loops - Design multi-agent systems and orchestration strategies |
| Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection |
| Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution |
| Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering |
| Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Implement guardrails and safety controls - Address ethical AI considerations and bias mitigation |
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NEW QUESTION # 42
An engineer inadvertently commits an API key to the repository by placing it in .claude/settings.json.
Which configuration design principle was violated?
Answer: B
Explanation:
A shared project settings file is intended to be committed so that team-level configuration can travel with the repository. Consequently, it must never contain API keys, access tokens, passwords, or comparable secrets.
Credentials should be injected at runtime through an approved secret-management mechanism, environment- specific credential provider, or delegated authentication flow. This separates distributable configuration from sensitive authentication material and supports rotation, revocation, auditing, and least privilege. Options A and B concern configuration precedence rather than secret handling. Option D is incorrect because MCP server definitions may legitimately be stored in project configuration; only their sensitive credential values must remain outside version control. Because the key was exposed, the operational response should also include immediate revocation or rotation and investigation of repository history. Claude Code: Settings files and precedence
NEW QUESTION # 43
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 # 44
A business sponsor has requested an AI solution to "improve customer experience." The sponsor cannot articulate which customer journey is failing, which metric reflects the failure, or which decisions the AI should support. The sponsor is asking you to begin design work next week.
Which delegation-competency action should you take first?
Answer: B
Explanation:
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.
NEW QUESTION # 45
You are preparing a HIPAA-eligible deployment for a healthcare customer.
Which configuration supports HIPAA compliance using Anthropic-offered tools?
Answer: C
Explanation:
Option B is the only configuration establishing the contractual, data-handling, and audit foundations needed for a HIPAA-eligible deployment. Anthropic requires an eligible commercial organization to activate the HIPAA-ready configuration and accept a Business Associate Agreement. A standard Enterprise subscription is not automatically covered merely because it is an enterprise product. Anthropic BAA Guidance Zero Data Retention may be required for particular covered services, including eligible Claude Code configurations, and is enabled per organization for qualified accounts. Audit logging supports access review, configuration monitoring, incident investigation, and compliance evidence. Anthropic provides audit logs for Enterprise organizations. Claude Code Legal and Compliance , Access Audit Logs Options A and D use consumer or personal accounts without the required contractual framework. Option C incorrectly treats the elimination of audit evidence as a substitute for a BAA and compliant configuration.
The signed BAA remains the authoritative source for covered products and features. The customer must also configure access control, minimum-necessary data use, workforce policies, retention, integrations, incident response, and vendor-risk controls. The configuration supports compliance; it does not independently guarantee it.
Study Guide references/topics: HIPAA readiness; BAA; ZDR; PHI handling; audit logging; eligible services; shared compliance responsibility.
NEW QUESTION # 46
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: B
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 # 47
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