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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Models, Prompting & Context Engineering | 13% | - Claude model selection and trade-offs - Context window optimization - Guardrails - System prompts and prompt templates - Prompt reuse and context engineering strategies |
| Topic 2: Governance, Safety & Risk Management | 14% | - Security and risk management - Ethical AI considerations - Regulatory and compliance requirements - Human-in-the-loop validation - AI safety and guardrails |
| Topic 3: Evaluation, Testing & Optimization | 16% | - Evaluation framework design - A/B testing - Evaluation metrics and datasets - Production monitoring and optimization - System issue diagnosis - Cost and performance optimization |
| Topic 4: Stakeholder Communication & Lifecycle Management | 14% | - Architecture documentation - Discovery and requirements gathering - Stakeholder management - Communicating architectural decisions - Service-level agreements - Solution lifecycle management |
| Topic 5: Integration | 19% | - Authentication and authorization analysis - RAG pipeline design
- Claude integration mechanisms
|
| Topic 6: Solution Design & Architecture | 17% | - Decomposition techniques for complex problem solving - Architectural patterns
- Alignment with business value, cost, performance, and SLAs - End-to-end architecture design - Translating business problems into Claude-based AI solutions |
| Topic 7: Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - AI-assisted developer workflows - Developer enablement - Claude tooling configuration for teams |
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NEW QUESTION # 34
You are compiling a diagnostic toolkit for Claude Code operational issues.
Which two diagnostic actions belong in the toolkit? (Select two.)
Each correct answer presents a complete solution.
Answer: B,D
Explanation:
A minimal reproduction and MCP status inspection are foundational diagnostic actions because they produce direct, interpretable evidence. A minimal reproduction removes unrelated prompts, tools, settings, hooks, and repository state while changing one variable at a time. This determines whether the failure follows a particular configuration, server, command, model request, or project artifact. It also creates a reproducible evidence package if escalation later becomes necessary.
MCP inspection addresses a common Claude Code failure domain. `claude mcp list` displays configured servers and their health states, including connected, authentication-required, pending-approval, and failed-to- connect conditions. `claude mcp get < name > ` provides server-specific details, while `/mcp` exposes status within an interactive session. These checks distinguish model behavior from transport, authentication, configuration, and workspace-trust failures.
Increasing temperature reduces repeatability and introduces additional variation, making root-cause isolation more difficult. Filing a support ticket before collecting local evidence reverses the appropriate diagnostic sequence. An immediate rollback may eventually be used as a controlled version comparison, but it changes the environment before a baseline has been captured and is therefore not a primary toolkit action.
Study Guide references/topics: [Managing and diagnosing MCP servers](https://docs.anthropic.com/en/docs
/claude-code/mcp); minimal reproducible cases; controlled-variable troubleshooting; authentication and connection-state diagnostics.
NEW QUESTION # 35
During an architectural review, the security team identifies a risk that adversarial content injected into retrieved documents could manipulate the model's behavior.
Which mitigation most directly addresses this threat?
Answer: B
Explanation:
The scenario describes indirect prompt injection: malicious instructions are embedded in third-party content that Claude processes as retrieved evidence. Option A addresses the threat at the appropriate boundary by classifying retrieved material as untrusted, screening it before use, and validating the model's output before it can influence downstream actions.
Anthropic recommends placing untrusted third-party content in clearly identified tool-result structures, explicitly instructing Claude that retrieved content cannot override system or user instructions, screening tool output for injection attempts, and applying output validation. Mitigate Jailbreaks and Prompt Injections Citations in Option B improve factual traceability but do not prevent embedded instructions from attempting to redirect model behavior. An outbound destination allow-list in Option C limits the damage that a compromised agent can cause through tool calls, but it does not directly detect or neutralize the injected content. Option D is valuable for detecting behavioral regression after a model change, not for controlling a specific malicious document during runtime.
A production implementation should combine content provenance, structural separation, injection screening, least-privilege tools, output-schema validation, and human confirmation for consequential actions.
Study Guide references/topics: Indirect prompt injection; untrusted retrieved content; input classification; output validation; layered runtime guardrails.
NEW QUESTION # 36
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: A
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 # 37
After a prompt-template update, several previously passing test cases now produce unexpected outputs.
Which test type is specifically designed to detect this category of failure?
Answer: D
Explanation:
Regression tests determine whether behavior that previously met requirements still passes after a change. A stable reference set provides identical inputs, expected outcomes, and scoring rules across prompt versions, allowing the team to attribute failures to the update. Integration tests verify interactions among components but may not measure detailed output behavior. Adversarial tests target attacks and misuse cases, while smoke tests establish only that essential system paths remain available. The failed cases should be added to or retained within the regression suite, and the old and new prompt should be compared under the same model, parameters, tools, and retrieval context. Anthropic describes regression evaluations as protection against behavioral backsliding. Demystifying evals for AI agents
NEW QUESTION # 38
A Claude architect observes that after a recent model-version upgrade, grounded responses began including claims not supported by the retrieved source documents.
Which mitigation is most directly targeted at this failure mode?
Answer: D
Explanation:
The observed failure is a groundedness defect: the response contains claims that cannot be traced to retrieved evidence. Option A directly constrains generation to the supplied sources, requires visible attribution, and verifies that each material claim has supporting text. Unsupported claims should be removed, qualified, or replaced with an explicit statement that the available sources are insufficient. Regression evaluation in Option B may detect the failure but does not itself prevent unsupported claims. OAuth addresses identity and authorization, while input classifiers address malicious or unsafe retrieved content; neither verifies factual support. Anthropic recommends citations, supporting quotations, and post-generation claim verification to reduce hallucinations in document-grounded applications. Reducing hallucinations
NEW QUESTION # 39
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