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| Section | Weight | Objectives |
|---|---|---|
| Solution Design & Architecture | 17% | - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions - Design multi-agent systems and orchestration strategies - Design end-to-end architectures and feedback loops - Select architectural patterns: workflow, agentic, augmented LLM |
| Stakeholder Communication & Lifecycle Management | 14% | - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases |
| Developer Productivity & Operational Enablement | 7% | - Configure Claude tools and environments for teams - Support debugging, monitoring, and operational resolution - Improve developer workflows with AI-assisted tooling |
| Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques |
| 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 |
| Governance, Safety & Risk Management | 14% | - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Manage data privacy and security compliance - Implement guardrails and safety controls - Address ethical AI considerations and bias mitigation |
| 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 |
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NEW QUESTION # 90
You are choosing the level of detail for an implementation guide. The audience is a delivery team that will build the deployment.
Which guidance composition best serves them?
Answer: D
Explanation:
A delivery team needs implementation-level direction rather than an executive architecture summary.
Component responsibilities establish ownership and boundaries, while interface contracts define schemas, accepted inputs, outputs, errors, and behavioral expectations. Sequence diagrams show how components interact across dominant and failure flows. Configuration tables with defaults prevent inconsistent assumptions between environments. Operational runbooks ensure that deployment, recovery, monitoring, and routine maintenance are addressed during implementation rather than improvised after release. Options B, C, and D omit one or more elements required to translate architectural intent into a buildable and supportable system. The guide should enable normal delivery work without continuous dependence on undocumented knowledge held by the architect.
Study Guide references/topics: Implementation documentation; component contracts; sequence diagrams; configuration management; operational readiness.
NEW QUESTION # 91
You are compiling guardrail tactics for a customer-facing assistant.
Which two tactics belong on the guardrail list?
Each correct answer presents a complete solution.
Answer: B,E
Explanation:
Option C introduces a deterministic validation boundary between probabilistic model output and downstream execution. Structured validation can reject missing fields, invalid types, unauthorized actions, malformed arguments, and values outside approved enumerations before any tool or business process acts on the response. Anthropic recommends Structured Outputs when guaranteed schema conformance is required.
Increase Output Consistency
Option D implements defense in depth. Prompt-level instructions establish intended behavior, while runtime classifiers, content filters, policy engines, output validators, tool permissions, and human confirmation gates provide controls outside the model. Anthropic's guardrail guidance recommends combining safeguards rather than depending on one mechanism. Mitigate Jailbreaks and Prompt Injections Option A creates a bypass mechanism that adversaries may discover or imitate. Option B may alter output variability but is not a security boundary and cannot guarantee policy compliance. Option E places complete reliance on a system prompt, which remains vulnerable to ambiguity, conflicting inputs, model errors, and indirect prompt injection.
Guardrails should be risk-based, independently enforceable where possible, observable, tested against adversarial cases, and proportional to the consequences of downstream actions.
Study Guide references/topics: Layered guardrails; structured validation; runtime content checks; prompt injection; deterministic enforcement; defense in depth.
NEW QUESTION # 92
You are investigating an MCP server that fails on first launch but succeeds on subsequent runs. System permission dialogs appeared during the first launch.
Which response is most appropriate?
Answer: D
Explanation:
The observed sequence provides direct causal evidence: the initial connection was interrupted while the operating system awaited authorization, and later attempts succeeded after permission was granted. The correct response is to verify repeatability, document the first-run requirement, and incorporate it into installation or onboarding instructions. Reinstalling the operating system is disproportionate and destroys useful diagnostic state. Disabling permission dialogs weakens endpoint security and bypasses the control rather than addressing expected behavior. Replacing a server that operates correctly after authorization misclassifies a transient initialization condition as a permanent defect. Claude Code uses explicit permission controls to balance capability and safety, so first-use approval behavior should be accommodated rather than suppressed. Claude Code permissions
NEW QUESTION # 93
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?
Answer: D
NEW QUESTION # 94
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: B
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 # 95
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