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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions - Align solutions to business value pillars - Design multi-agent systems and orchestration strategies |
| Topic 2: Claude Models, Prompting & Context Engineering | 13% | - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails |
| Topic 3: 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 |
| Topic 4: Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Implement guardrails and safety controls - Manage data privacy and security compliance |
| Topic 5: Integration | 19% | - Integrate Claude with enterprise systems, APIs, and tools - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability - Implement Model Context Protocol (MCP) integrations |
| Topic 6: Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection |
| Topic 7: Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases |
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NEW QUESTION # 78
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: B
NEW QUESTION # 79
You are presenting an architectural decision to a mixed audience that includes an executive sponsor and the engineering leads who will implement the decision.
Which presentation strategy best serves both audiences?
Answer: B
Explanation:
Option B uses progressive disclosure to meet the decision needs of both audiences. The executive sponsor first receives the selected direction, intended business outcomes, major constraints, cost or performance implications, and accepted trade-offs. This establishes why the decision matters and whether it remains aligned with strategic objectives.
The presentation can then move into the technical rationale required by engineering leads: component responsibilities, integration contracts, model and retrieval choices, security controls, rejected alternatives, implementation dependencies, evaluation gates, operational consequences, and migration requirements. This structure gives engineers enough evidence to implement the decision without forcing the executive audience to interpret unnecessary low-level detail.
Option A excludes the sponsor's concerns and fails to connect technical design to business value. Option C removes the context required for governance and implementation, making the decision appear arbitrary.
Option D assumes that all participants require identical information density, which usually produces a presentation that is too technical for executives and insufficiently precise for engineers.
The strongest communication pattern is layered but internally consistent: one decision, one rationale, and multiple levels of detail tailored to the responsibilities of the audience.
Study Guide references/topics: Mixed-audience communication; progressive disclosure; business outcomes; architectural trade-offs; technical rationale; implementation implications.
NEW QUESTION # 80
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 # 81
An engineering organization is adopting Claude Code across 200 developers. A team lead proposes that AI- generated pull requests bypass standard code review for changes under 50 lines because small changes are considered low risk and review capacity is constrained.
Which two Diligence-competency objections should you raise? (Select two.) Each correct answer presents part of the solution.
Answer: C,D
Explanation:
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
NEW QUESTION # 82
You are a solution architect designing a Claude-based assistant with access to 60 internal tools across multiple business domains. Loading every tool definition on every request increases token usage and time to first response.
Which design pattern best addresses this issue without sacrificing capability breadth?
Answer: B
Explanation:
Progressive tool discovery preserves the complete capability catalog while limiting initial context to a curated set of common common common tools and a discovery mechanism. When the task requires an additional capability, Claude searches the catalog and loads only the relevant tool definitions.
Anthropic's Tool Search mechanism implements implements this pattern through deferred loading. The context initially contains the search search search tool and any deliberately non-deferred tools; relevant definitions are expanded only when discovered. This reduces token consumption and helps maintain tool- selection accuracy in large catalogs. ToolTool Search Tool Option B adds a model call and still repeatedly processes all definitions, increasing latency latency latency and cost while potentially losing important schema details. Option C treats a larger context window as permission to waste context and does not address time-to-first-response overhead. Option D reduces capability breadth because the fixed five tools may be irrelevant to a given task.
The architect should provide clear tool and server server server descriptions, define which high-frequency tools remain immediately available, and evaluate discovery recall, selection accuracy
NEW QUESTION # 83
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