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| Section | Weight | Objectives |
|---|---|---|
| Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Manage data privacy and security compliance - Address ethical AI considerations and bias mitigation - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Developer Productivity & Operational Enablement | 7% | - Support debugging, monitoring, and operational resolution - Improve developer workflows with AI-assisted tooling - Configure Claude tools and environments for teams |
| Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks |
| 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% | - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions - Design multi-agent systems and orchestration strategies - Select architectural patterns: workflow, agentic, augmented LLM - Design end-to-end architectures and feedback loops |
| Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering - Document architectures and support full lifecycle phases |
| Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection - Define evaluation metrics and success criteria - Implement iterative improvement pipelines |
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NEW QUESTION # 125
You are selecting the documentation set that should accompany a Claude-based deployment at handoff.
Which set is most complete?
Answer: A
Explanation:
A complete handoff package must help different stakeholders understand, operate, troubleshoot, and evolve the Claude-based solution. An architecture overview explains the system's structure and responsibilities, while Architecture Decision Records document important decisions, alternatives, and trade-offs. Component- level diagrams provide implementation context without forcing maintainers to reconstruct dependencies from source code. Operational runbooks and an on-call playbook enable reliable incident detection, diagnosis, escalation, and recovery. Documenting known limitations is particularly important for Claude applications because model behavior can vary with prompts, context, input distribution, and external dependencies.
Options B, C, and D each provide only a partial or temporary understanding of the system. They would create knowledge gaps, increase operational risk, and make future maintenance dependent on individuals rather than durable organizational documentation.
NEW QUESTION # 126
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 # 127
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: D
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 # 128
A senior architect is managing stakeholder expectations for a Claude-based reporting assistant midway through development. Stakeholders have escalating concerns about response latency.
Which two actions most directly address stakeholder expectation alignment in this situation? (Select two.)
Answer: D,E
Explanation:
Expectation alignment begins with measured evidence. Option A gives stakeholders the median experience through p50 latency and the slower-tail experience through p95, then compares both with the agreed SLA.
The measurements should reflect representative input sizes, concurrency, retrieval activity, tool calls, geographic routing, and cache conditions.
If the target remains infeasible after reasonable optimization, Option E establishes the correct governance response. The architect should collaboratively revise the SLA, scope, cost envelope, model choice, or user- experience design rather than silently accepting a breach or making unilateral changes. Any revision should document the business impact and accepted trade-offs.
Option B commits all resources before the problem's severity, causes, and business priority have been established. Option C wrongly treats latency as uncontrollable; model selection, prompt length, retrieval depth, caching, streaming, and architecture can materially affect it. Option D introduces an unvalidated provider and could create quality, security, compliance, and integration regressions.
Expectation management is not merely communicating bad news. It requires transparent measurements, credible optimization options, explicit trade-offs, and jointly approved commitments that the production architecture can actually satisfy.
Study Guide references/topics: SLA alignment; p50 and p95 latency; stakeholder negotiation; evidence-based communication; architectural trade-offs; production constraints.
NEW QUESTION # 129
You are integrating Claude Code into a workflow that runs against a production database.
Which guardrail design most directly preserves safety on data-modifying operations?
Answer: A
Explanation:
Option D applies three complementary controls. First, a read-only database credential creates an authorization boundary outside the model; prompt instructions alone cannot convert that credential into write access.
Second, restricting the subagent's tool list reduces capability exposure by preventing the agent from selecting unrelated or unnecessarily privileged operations. Third, explicit human confirmation creates a deliberate approval gate before any exceptional data-changing action is executed.
This is defense in depth. If Claude misinterprets a request or processes malicious instructions from untrusted content, the restricted credential and tool configuration limit the available action surface. Human review then protects operations with potentially irreversible production consequences. Logging and audit trails should remain enabled to record the actor, request, tool call, approval, affected records, and outcome.
Anthropic documents that Claude Code begins with read-only permissions in Manual mode and requests approval for actions that modify the environment. Its permission system also supports granular allow, ask, and deny controls for MCP tools and subagents. Claude Code Security , Configure Permissions Options A and B eliminate least privilege and approval boundaries. Option C removes essential detection and forensic evidence.
Study Guide references/topics: Production database safety; least privilege; read-only defaults; MCP permissions; subagent scoping; human confirmation; auditability.
NEW QUESTION # 130
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