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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integration | 19% | - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations - Integrate Claude with enterprise systems, APIs, and tools |
| Topic 2: Evaluation, Testing & Optimization | 16% | - Define evaluation metrics and success criteria - Implement iterative improvement pipelines - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection |
| Topic 3: Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs |
| Topic 4: Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Implement guardrails and safety controls |
| Topic 5: Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution |
| Topic 6: Solution Design & Architecture | 17% | - Translate business problems into Claude-based AI solutions - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Design multi-agent systems and orchestration strategies - Design end-to-end architectures and feedback loops |
| Topic 7: Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs |
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NEW QUESTION # 112
You are supporting an engineer whose Claude Code session reports a permission denial when the engineer expected the action to be allowed.
Which resolution step is most appropriate?
Answer: D
Explanation:
Claude Code permission decisions can be affected by multiple configuration scopes. The appropriate diagnostic action is to inspect the effective rules and identify the exact deny pattern. Managed policy has the highest precedence, and a deny rule from any applicable scope cannot be overridden by an allow rule elsewhere. The architect must then determine whether the denial represents an intentional security boundary or an incorrectly scoped rule. If correction is justified, only the specific rule should be amended. Repeated retries cannot change a deterministic permission decision. Disabling permissions or granting unrestricted Bash access removes safeguards far beyond the reported problem and violates least privilege. Anthropic's permission documentation explicitly describes scope precedence and deny-first evaluation. Claude Code permissions
NEW QUESTION # 113
You are supporting a team whose Claude Code sessions consistently load 60 or more MCP tools from many servers, exhausting context budget before the session begins.
Which adjustment most directly addresses this issue without removing capability?
Answer: D
Explanation:
Tool Search applies progressive discovery to a large tool catalog. Instead of inserting every MCP tool's complete JSON schema and description into the initial context, Claude receives limited discovery information and loads the full definitions only when a task requires them. This preserves the available context for instructions, repository content, conversation history, and tool results without removing the underlying capabilities.
Anthropic's Claude Code documentation states that Tool Search keeps MCP context usage low by deferring tool definitions until Claude needs them. Only the tools actually selected enter the working context. Claude Code MCP Tool Search Anthropic's advanced-tool-use guidance also describes conventional multi-server configurations consuming tens of thousands of tokens before work begins, while on-demand discovery substantially reduces that overhead. Advanced Tool Use Option B solves context pressure by destroying required functionality. Option C worsens the constraint by adding more prompt tokens. Option D expands the exact source of context bloat.
The team should also improve server and tool descriptions so discovery returns the correct tools, monitor tool- selection quality, and keep frequently required foundational tools non-deferred only when evaluations justify the additional context cost.
Study Guide references/topics: MCP scaling; Tool Search; progressive discovery; deferred schemas; context- budget management; tool-selection accuracy.
NEW QUESTION # 114
You are operating an interactive assistant whose dominant performance constraint is per-turn latency. Quality on routine turns is already acceptable.
Which configuration adjustment most directly improves latency without disproportionately damaging quality?
Answer: A
Explanation:
Option B targets two major contributors to per-turn latency: retrieved-context size and repeated prompt processing. Reducing retrieval to an empirically validated top- k limits the tokens Claude must process while preserving the passages that historically provide sufficient answer coverage. Caching the stable system- prompt prefix avoids repeatedly processing identical instructions.
Anthropic's latency guidance recommends choosing an appropriate model and reducing unnecessary input and output tokens. Its prompt-caching documentation explains that caching reusable prompt prefixes can reduce both repeated processing cost and latency. Reducing Latency , Prompt Caching The retrieval depth must be selected through evaluation rather than arbitrary truncation. The team should compare answer quality, retrieval recall, latency, and failure rates across candidate values and retain escalation logic for complex queries requiring deeper retrieval.
Option A removes an applicable latency optimization. Option C maximizes context consumption regardless of demonstrated benefit. Option D deliberately selects the slowest model class even though routine-turn quality already meets requirements.
Study Guide references/topics: Latency optimization; top- k retrieval; prompt caching; token reduction; quality-latency evaluation; adaptive retrieval.
NEW QUESTION # 115
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?
Answer: B
Explanation:
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.
NEW QUESTION # 116
Engineering leadership wants to roll out Claude Skills to 280 developers across 14 teams. Skills will encode internal coding standards, code-review checklists, and incident-postmortem templates. Leadership has asked how to govern Skill authorship so that Skills remain trustworthy without bottlenecking on a single central team.
Which governance model should you recommend?
Answer: B
Explanation:
Federated authorship allows each domain team to maintain the procedural knowledge it understands while preserving organization-wide trust through a central publication gate. Team authors can update coding standards, review practices, and postmortem procedures without waiting for a single platform group to write every change. Central reviewers can validate ownership, security, tool permissions, duplication, versioning, testing evidence, and compliance before publication. Per-developer or fully decentralized publication creates inconsistent and potentially unsafe Skills. Fully centralized authorship becomes a scalability bottleneck and separates content maintenance from domain expertise. Anthropic describes Skills as packages of instructions, scripts, and resources and warns that repository Skills may grant broad tool access, making formal review essential. Claude Code Skills
NEW QUESTION # 117
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