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| Section | Weight | Objectives |
|---|---|---|
| Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools |
| Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Translate business problems into Claude-based AI solutions - Align solutions to business value pillars - Design end-to-end architectures and feedback loops - Design multi-agent systems and orchestration strategies |
| 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 |
| 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 |
| Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases |
| Governance, Safety & Risk Management | 14% | - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Manage data privacy and security compliance |
| 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 # 70
You are building an ethics review checklist for deployments supported by artificial intelligence (AI). Which two checks belong on the list? (Select two.) Each correct answer presents a complete solution.
Answer: A,B
Explanation:
High-impact AI-supported decisions must retain clearly assigned human accountability. Claude may provide recommendations, classifications, or generated content, but responsibility for consequential outcomes cannot be transferred to the model. An ethics review should also investigate whether outputs make unsupported generalizations about individuals or groups, because such behavior can produce discriminatory, misleading, or harmful outcomes. Therefore, B and E directly address ethical accountability and fairness. Licensing in option A is an important legal and procurement concern, but it is not the strongest ethics-focused control presented.
Latency and throughput in option D are operational performance requirements. Option C is inappropriate because ethics risks are not limited to outputs above a confidence threshold; confidence values may also be poorly calibrated and do not prove that an output is fair, accurate, or ethically acceptable.
NEW QUESTION # 71
You are identifying inefficiency in a Claude Code workflow where each engineer manually re-explains the project's conventions and architecture in every session.
Which adjustment most directly removes this inefficiency?
Answer: B
Explanation:
A project-scoped CLAUDE.md converts repeatedly supplied information into persistent, version-controlled context. Claude Code loads applicable CLAUDE.md instructions at session initialization, allowing architecture principles, coding conventions, build commands, repository structure, and verification requirements to be shared consistently across engineers. Anthropic explicitly recommends using CLAUDE.
md for information that developers would otherwise explain repeatedly; concise and specific instructions provide stronger adherence. Committing the file also enables normal review and change-control practices.
Option C reduces typing speed but preserves the underlying waste and inconsistency. Options A and D remove useful capability or governance rather than correcting the workflow. The implementation should periodically review the file for obsolete, conflicting, or unnecessarily verbose instructions.
Study Guide references/topics: Claude Code project memory ; CLAUDE.md; persistent context; repository conventions; workflow standardization.
NEW QUESTION # 72
You are configuring tool permissions for a Claude-based assistant. The assistant's defined responsibilities require read access to a knowledge base and write access to a draft queue, and nothing else.
Which scoping design best applies least privilege?
Answer: C
Explanation:
Least privilege means granting the exact capabilities required for the defined responsibility-no more and no less. Option B permits the assistant to read the approved knowledge base and write only to the draft queue.
Enforcing the allow-list at the orchestration layer creates a deterministic authorization boundary outside the model's prompt and reasoning.
The rule should also be role-specific so different users or operational roles receive only the tools and data scopes they require. The knowledge-base tool should remain read-only, while the draft-queue tool should be restricted to draft creation rather than external sending, approval, or production publication.
Option A exposes every globally readable tool, creating unnecessary access to unrelated information. Option C relies on voluntary model behavior instead of enforceable authorization. A mistaken tool selection or successful prompt injection could therefore invoke an inappropriate capability. Option D minimizes permissions by eliminating the assistant's required functionality and is not a viable least-privilege implementation.
Anthropic's permission guidance distinguishes allow, ask, and deny rules and confirms that these controls are enforced by Claude Code rather than by model instructions. Configure Permissions Study Guide references/topics: Least privilege; per-role authorization; orchestration-layer enforcement; capability scoping; read/write separation; tool allow-lists.
NEW QUESTION # 73
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
Explanation:
Option C addresses both components responsible for the excessive cost. Anthropic prompt caching allows stable, repeatedly submitted prompt material-such as system instructions, tool definitions, and reusable background information-to be placed in a consistent prefix. After that prefix is written to the cache, qualifying subsequent requests can reuse it at the lower cache-read cost instead of repeatedly processing the same content at the standard input-token rate.
Retrieval must be optimized separately. Supplying an entire document collection or excessively deep search results increases cost, consumes context capacity, and may reduce answer quality by surrounding the relevant evidence with distracting material. Retrieval should select the smallest set of authoritative passages that provides sufficient evidence for the current query. This typically requires relevance scoring, deduplication, metadata filtering, reranking, and explicit token-budget limits.
Increasing retrieval depth or expanding the repeated system prompt directly worsens the identified cost driver.
Moving every request to a heavier model changes the unit economics but does not correct inefficient context construction. The recommended optimization therefore combines prefix caching with query-specific context pruning, followed by evaluation to confirm that reduced context does not lower task accuracy.
Study Guide references/topics: [Prompt caching](https://docs.anthropic.com/en/docs/build-with-claude
/prompt-caching); [effective context engineering] (https://www.anthropic.com/engineering/effective-context- engineering-for-ai-agents); retrieval precision; token-cost analysis; cache-prefix stability.
NEW QUESTION # 74
A technical team is cataloging risks specific to Claude's use in a document-grounded Q & A system.
Which two items represent failure modes that are intrinsic to LLM-based systems rather than generic software defects? (Select two.)
Answer: A,E
Explanation:
Options A and B describe model-specific behavioral risks. An overly broad safety response is a false positive caused by how the language model interprets contextual or surface-level signals. A plausible but unsupported answer is a hallucination: the generated statement appears credible but lacks grounding in the retrieved evidence. Anthropic recommends grounding factual responses in source material, requiring citations, allowing Claude to express uncertainty, and verifying generated claims. Options C, D, and E are conventional infrastructure or application failures involving certificates, routing, and database connectivity. They can occur in systems that do not use an LLM and should be addressed through standard monitoring, configuration management, retry policies, and operational testing. Anthropic: Reduce hallucinations
NEW QUESTION # 75
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