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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Implement guardrails and safety controls - Address ethical AI considerations and bias mitigation - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Topic 2: Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Manage stakeholder feedback and expectation alignment - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering |
| Topic 3: 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 - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars |
| Topic 4: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails |
| 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: Evaluation, Testing & Optimization | 16% | - Optimize performance, prompting, and model selection - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Implement iterative improvement pipelines |
| Topic 7: 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 |
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NEW QUESTION # 129
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 # 130
You are diagnosing a Claude Code session whose subagent uses 50,000 tokens of context before the engineer types a single message.
Which root cause is most likely?
Answer: B
Explanation:
Claude Code constructs the model context before the first user message. That initial context can include the system prompt, project instructions, subagent configuration, MCP server instructions, and tool definitions expressed as JSON schemas. When numerous MCP servers expose many tools and Tool Search is disabled, those schemas must be loaded upfront. A large collection of verbose tool descriptions and parameter schemas can therefore consume tens of thousands of tokens before the engineer enters any text.
Tool Search is specifically designed to prevent this form of context inflation. With Tool Search enabled, Claude initially receives only server instructions and compact tool-identification information; full tool schemas are deferred until a relevant tool is discovered and required. Anthropic consequently describes Tool Search as the mechanism for keeping MCP context consumption low as integrations scale.
Keyboard layouts, font rendering, and display scaling operate outside the language-model context and cannot generate API input tokens. Likewise, model behavior does not silently reserve 50,000 tokens for personal preferences. The diagnostic action is to inspect configured MCP servers and tool counts, determine whether Tool Search has been disabled, and remove or defer unnecessary definitions.
Study Guide references/topics: [MCP Tool Search and context scaling](https://docs.anthropic.com/en/docs
/claude-code/mcp); MCP server configuration; subagent context budgeting; tool-schema optimization.
NEW QUESTION # 131
The compliance team at a firm has approved a Claude Skill that generates client-facing investment summaries. The Skill includes the firm's required disclaimers and prohibited-language list. A product manager has asked whether additional guardrails are needed at the application layer or whether the Skill alone is sufficient.
Which two guardrail responsibilities should remain at the application layer rather than the Skill? (Select two.) Each correct answer presents part of the solution.
Answer: D,E
Explanation:
Authorization and compliance logging must remain enforceable application-layer controls. Option B requires the application to authenticate the requester, resolve the user's role and entitlements, and deny the operation when the user lacks authority. A Skill can instruct Claude how to behave, but it is not a reliable security boundary and cannot replace identity-based access control enforced by the surrounding system.
Option A similarly belongs at the application layer. The application must create an authoritative audit event containing the requester, timestamp, Skill and model version, relevant input or content identifiers, generated output, review status, and retention classification. Audit evidence cannot depend on whether the model remembers or chooses to invoke a logging instruction.
Options C, D, and E describe reusable generation behavior that appropriately belongs within the approved Skill: house style, the standardized disclaimer, and the prohibited-language policy. The application may still validate their presence after generation, but the Skill remains the primary reusable content package.
The correct architecture separates behavioral guidance from deterministic enforcement. Skills supply domain instructions and templates; the application enforces identity, authorization, audit retention, data access, output validation, and consequential-action controls.
Study Guide references/topics: Skills versus application controls; authentication; authorization; compliance logging; deterministic guardrails; policy enforcement boundaries.
NEW QUESTION # 132
You are classifying token-management tactics by where each tactic applies in the request lifecycle: "Input Preparation," "Prompt Construction," or "Output Handling."
Answer:
Explanation:
Explanation:
Persist the validated output for downstream consumption and audit - Output Handling Order prompt sections so cacheable content appears before per-request content - Prompt Construction Summarize prior conversation history when full history is no longer needed - Input Preparation Validate the model's structured output against the expected schema - Output Handling Move stable repeated content into a cacheable prefix at the beginning of the prompt - Prompt Construction Trim retrieved passages to spans relevant to the user's question - Input Preparation Input preparation determines what evidence and history should enter the request, so summarization and retrieval trimming belong there. Prompt construction determines ordering and cache boundaries; stable repeated instructions must precede dynamic per-request content to maximize cache reuse. Output handling begins after generation and includes schema validation, persistence, auditing, and downstream delivery.
Mixing these responsibilities creates inefficient prompts and weak validation boundaries. Anthropic's context guidance emphasizes curating the smallest useful context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly depends on a stable reusable prefix, while structured outputs or schema validation protect downstream systems from malformed responses. Context windows; prompt caching
NEW QUESTION # 133
A research summarization assistant has been deployed for six months. A user has flagged that a generated summary contained a fabricated citation. The product team has asked whether the incident requires architectural action or whether it is an isolated case.
Which two Diligence-competency actions should you take? (Select two.)
Each correct answer presents part of the solution.
Answer: A,D
Explanation:
A single confirmed fabrication requires investigation, but it does not establish the population-level failure rate. Sampling recent summaries determines prevalence, affected content types, and whether the incident correlates with particular prompts, documents, or model versions. The architecture must also be examined for grounding controls, including source-constrained generation, citation rendering, claim-to-source verification, and rejection of unsupported statements. Anthropic recommends making factual outputs auditable through citations and requiring supporting quotations for important claims. Immediate universal shutdown may become necessary if the risk is severe, but doing so before establishing scope is not the most disciplined first response. Options D and E dismiss evidence without validation and would prevent corrective action.
Reducing hallucinations
NEW QUESTION # 134
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