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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Manage data privacy and security compliance |
| Topic 3: Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs - Apply context engineering and context management techniques - Mitigate prompt injection, leaks, and jailbreak risks |
| Topic 4: Integration | 19% | - Integrate Claude with enterprise systems, APIs, and tools - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability |
| Topic 5: 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 |
| Topic 6: Solution Design & Architecture | 17% | - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions - Design end-to-end architectures and feedback loops - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies |
| Topic 7: Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution - Configure Claude tools and environments for teams |
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NEW QUESTION # 116
You are integrating Claude Code into a team workflow and must complete the design and configuration steps before piloting the integrated workflow with a small group.
Which two steps must be completed BEFORE piloting the integrated workflow? (Select two.) Each correct answer presents part of the solution.
Answer: B,E
Explanation:
The pilot must test a deliberately designed workflow, not technology added without role boundaries. Option B identifies where Claude provides measurable value and where approval, accountability, or consequential decisions must remain human responsibilities. Option E establishes the technical environment required for consistent pilot execution: repository-shared MCP servers, scoped permissions, specialized subagents, and persistent project instructions. Iteration in Option D necessarily follows observed pilot findings. Formal approval from every manager is unnecessarily broad unless required by organizational governance. Complete runbooks and final handoff documentation mature after the workflow has been validated, although essential pilot procedures must exist. Anthropic documents project-level instructions, permissions, MCP servers, and subagents as complementary mechanisms for team workflows. Claude Code extension overview
NEW QUESTION # 117
You are comparing patterns for a batch document-classification job that follows fixed steps: extract metadata, classify, summarize, and persist.
Which pattern is the best fit and why?
Answer: B
Explanation:
A workflow is appropriate when processing stages and transitions are known in advance. Metadata extraction, classification, summarization, and persistence can be represented as deterministic nodes with explicit schemas, validation rules, retry policies, and failure handling. This provides predictable execution, cost, observability, and testability without paying for repeated model-driven planning. Anthropic's Building Effective AI Agents distinguishes workflows, where predefined code paths control execution, from agents, where the model dynamically determines its process. Option A reaches the correct pattern for an incorrect reason: workflows and agents can both invoke tools. Options B and C introduce unnecessary autonomy and make unsupported claims about consistency or universal accuracy.
Study Guide references/topics: Workflow versus agentic patterns; deterministic orchestration; batch processing; structured outputs; predictable cost; error handling.
NEW QUESTION # 118
You are designing a content moderation classifier that processes high volumes of user-generated comments under a tight per-message latency budget using well-defined classification labels.
Which model selection best aligns with the workload?
Answer: D
Explanation:
Haiku is the appropriate starting point because the workload is high-volume, latency-sensitive, and based on a stable closed set of moderation labels. These characteristics favor a fast, cost-efficient model capable of consistent classification without incurring the additional inference time and expense associated with deeper reasoning.
Anthropic's model-selection guidance requires architects to balance capability, speed, and cost rather than automatically selecting the most capable model. Its content-moderation guidance specifically identifies Haiku as a cost-effective option for processing moderation workloads at substantial scale. Choosing the Right Model
, Content Moderation
Opus is disproportionate to a routine closed-set classification problem. Sonnet may become justified if evaluation demonstrates that Haiku fails materially on complex policy distinctions, multilingual ambiguity, or adversarial edge cases, but it should not be selected merely because it is larger. Enabling extended thinking on every request would further increase latency and token consumption without evidence that the additional reasoning improves the defined success metrics. The correct architectural practice is to establish a representative moderation evaluation set, validate Haiku against accuracy and safety thresholds, and escalate only the cases that genuinely need deeper reasoning.
Study Guide references/topics: Model selection; capability-latency-cost trade-offs; classification workloads; evaluation-driven routing; moderation architecture.
NEW QUESTION # 119
A Claude architect is configuring a shared Claude Code environment for a twelve-person development team.
Which two configuration decisions most directly establish a consistent, secure team environment? (Select two.)
Answer: D,E
Explanation:
Project-scope configuration is appropriate for shared MCP definitions, permissions, hooks, and other repository-specific behavior that every team member should receive consistently. Managed configuration complements this by enforcing organization-wide security and compliance requirements that individual developers cannot override. Together, these mechanisms separate collaborative project configuration from centrally controlled policy. Editor themes are personal preferences and do not materially establish a secure environment. Allowing each engineer to select an arbitrary model can undermine consistency when model choice affects behavior, cost, or approved-use requirements. Storing a shared API key in a committed project file is a serious credential-management failure. Claude Code documentation identifies .claude/settings.json as the shared project file and managed settings as the highest-precedence organizational policy layer. Claude Code: Settings files and precedence
NEW QUESTION # 120
You are evaluating retrieval-strategy claims used by a peer team.
For each claim, select yes if the statement is generally accurate. Otherwise, select no.
Answer:
Explanation:
Explanation:
Yes, Yes, Yes, No, No
Dense vector retrieval represents semantic similarity, making it suitable for matching paraphrases and conceptually related language even when the query and source do not share identical words. Sparse lexical retrieval, including BM25-style matching, retains strong sensitivity to exact tokens and is therefore valuable for identifiers, product codes, technical names, and uncommon terminology.
Structured query retrieval is appropriate when the required operation depends on explicit fields, predicates, joins, counts, grouping, or aggregation over a relational schema. In that situation, generating or invoking a constrained database query is more precise than approximating the operation through semantic similarity.
Hybrid retrieval is not identical to dense retrieval. It combines semantic and lexical candidate sets, typically followed by rank fusion or reranking. Anthropic's contextual-retrieval guidance explains that semantic search captures meaning and paraphrases, while BM25 captures exact terminology; combining them improves coverage. Contextual Retrieval, Contextual Retrieval Cookbook Random sampling is not a relevance strategy. It provides no systematic relationship between the query and selected evidence, producing unstable coverage and preventable hallucination risk. Production Q & A requires deterministic or evaluated relevance mechanisms, access filters, suitable indexes, and measurable retrieval metrics such as recall at k.
Study Guide references/topics: Dense retrieval; sparse retrieval; structured queries; hybrid search; rank fusion; retrieval evaluation; production RAG design.
NEW QUESTION # 121
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