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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Stakeholder Communication & Lifecycle Management | 14% | - Stakeholder management - Communicating architectural decisions - Architecture documentation - Discovery and requirements gathering - Solution lifecycle management - Service-level agreements |
| Topic 2: Governance, Safety & Risk Management | 14% | - Regulatory and compliance requirements - AI safety and guardrails - Security and risk management - Human-in-the-loop validation - Ethical AI considerations |
| Topic 3: Evaluation, Testing & Optimization | 16% | - Evaluation metrics and datasets - Evaluation framework design - Cost and performance optimization - Production monitoring and optimization - A/B testing - System issue diagnosis |
| Topic 4: Developer Productivity & Operational Enablement | 7% | - Claude tooling configuration for teams - AI-assisted developer workflows - Debugging and operational issue resolution - Developer enablement |
| Topic 5: Solution Design & Architecture | 17% | - Alignment with business value, cost, performance, and SLAs - Decomposition techniques for complex problem solving - Translating business problems into Claude-based AI solutions - Multi-agent systems and orchestration - Architectural patterns
|
| Topic 6: Integration | 19% | - Claude integration mechanisms
- RAG pipeline design
|
| Topic 7: Claude Models, Prompting & Context Engineering | 13% | - Context window optimization - Prompt reuse and context engineering strategies - System prompts and prompt templates - Guardrails - Claude model selection and trade-offs |
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NEW QUESTION # 30
A document analysis service processes legal filings averaging 80,000 tokens each. Each filing is queried by attorneys an average of 14 times during a case. The current architecture sends the full filing on every query.
The CFO has asked you to reduce per-query costs while preserving response quality. The security officer requires that filing contents not be stored outside Fabrikam's tenancy.
Which optimization approach should you recommend?
Answer: A
Explanation:
The 80,000-token filing is a large, stable prefix reused across fourteen requests, making prompt caching the strongest optimization. The first request creates the cache; subsequent identical-prefix requests use lower- priced cache reads while retaining the complete filing, preserving the evidence available to Claude. Anthropic documents cache reads at a fraction of standard input cost and confirms that caches are isolated by organization and, on supported platforms, by workspace. Cached representations remain in memory rather than persistent storage. Options A and C may omit context through summarization or retrieval and therefore require additional quality evaluation. Option D lowers unit cost but may reduce reasoning quality. The design must preserve prefix identity, choose an appropriate TTL, and verify the tenancy configuration.
Study Guide references/topics: Prompt caching ; cache isolation; stable prefixes; long-document analysis; cost optimization.
NEW QUESTION # 31
You are preparing an operational runbook for a Claude-based service.
Which content is essential to include in the runbook?
Answer: B
Explanation:
An actionable runbook must connect detection, diagnosis, containment, recovery, and escalation. Option B supplies that complete operational chain: alerts identify abnormal conditions; triage procedures guide initial investigation; dashboards and logs provide evidence; escalation paths assign authority; and rollback procedures restore a known-safe state. The runbook should cover model errors, latency degradation, rate limits, tool failures, prompt regressions, cost anomalies, data-governance events, and dependency outages.
Anthropic's operational materials demonstrate incident workflows in which alerts lead responders to logs and runbooks before corrective action. Options A, C, and D each omit information required for an on-call engineer to progress from notification to controlled resolution. Procedures should be tested through exercises and updated after incidents.
Study Guide references/topics: Claude operational and observability patterns ; incident response; triage; escalation; rollback; operational readiness.
NEW QUESTION # 32
An architect is reviewing a Claude-based candidate-screening tool prior to deployment. A stakeholder asserts that because the model was not trained on company data, no bias evaluation is necessary.
Which two responses most accurately challenge this assertion? (Select two.)
Answer: A,B
Explanation:
The absence of company-specific training does not establish unbiased behavior. A foundation model may reflect demographic associations or unequal treatment patterns originating from pretraining data, model behavior, prompt framing, retrieval content, or the surrounding decision workflow. Because candidate screening can materially affect employment opportunities, the complete system must be evaluated across relevant protected groups before deployment. Appropriate measures include selection-rate differences, false- positive and false-negative rates, calibration, intersectional slices, and human override patterns. Disclosure that AI is used does not replace empirical fairness testing. Protected-class labels need not have been explicit in training data for proxy variables or inferred attributes to produce disparities. Provider policies similarly do not validate a customer's specific implementation. Anthropic system cards document discriminatory-bias evaluations, reinforcing the need for application-level testing. Claude model system card
NEW QUESTION # 33
You are selecting a pattern for a compliance Q & A assistant that must answer policy questions with citations to the authoritative internal source set. Latency, cost, and audit predictability are prioritized.
Which pattern is the best fit?
Answer: D
Explanation:
A constrained retrieval-augmented generation architecture best satisfies the grounding, citation, latency, cost, and audit requirements. The system first indexes the approved internal policy corpus. At request time, it retrieves a small set of relevant passages, supplies those passages to Claude with stable source identifiers, and requires the answer to cite the supporting material. Anthropic's search-result content blocks are specifically designed for this pattern: they allow custom RAG systems to provide source-attributed internal content and enable Claude to render citations against those results.
The retrieval boundary is critical. Only approved and version-controlled policy sources should be searchable, with document identifiers, effective dates, access controls, retrieval scores, and cited passages retained for audit reconstruction. This produces substantially more predictable evidence than unconstrained web research.
A planner-researcher-writer system introduces unnecessary model calls, orchestration complexity, cost, latency, and behavioral variance for routine policy lookup. Open-web browsing violates the authoritative- source requirement and increases prompt-injection and provenance risk. Concatenating the complete corpus into every prompt wastes tokens, increases latency, and can reduce retrieval precision as the corpus expands.
Study Guide references/topics: Search-result blocks for cited RAG ; Claude citations ; authoritative-corpus retrieval; source attribution; auditable context engineering.
NEW QUESTION # 34
A security audit uncovers two issues: (1) all end users share a single API key, and (2) tool calls are executed without logging the initiating user.
Which two mitigations directly address these specific findings? (Select two.)
Answer: D,E
Explanation:
Per-user, scope-restricted OAuth tokens directly eliminate the shared-credential weakness. Each token represents a distinct user or delegated identity, carries only the permissions required for that user's role, and can be revoked independently. This supports least privilege, limits the blast radius of credential compromise, and enables authorization decisions to be tied to a specific principal instead of an undifferentiated shared API key.
Actor attribution in tool-call logs directly resolves the second finding. Each audit event should record the authenticated user, delegated service identity, session or request identifier, tool name, authorization decision, relevant resource, timestamp, and outcome. This establishes accountability and permits incident investigators to reconstruct who initiated a consequential action.
Moving credentials into a server-side secret store is sound secret-management practice, but it does not by itself correct the shared-identity problem: a centrally stored key can still represent every user as the same principal. Schema validation protects downstream processing, while retrieval-layer RBAC protects information access; neither addresses the two specific audit findings.
These controls should be reinforced by short-lived tokens, server-side authorization enforcement, sensitive- field redaction, and tamper-resistant audit-log retention.
Study Guide references/topics: [Claude Code security](https://docs.anthropic.com/en/docs/claude-code
/security); [MCP authentication and OAuth] (https://docs.anthropic.com/en/docs/claude-code/mcp); least privilege; identity propagation; auditable tool execution.
NEW QUESTION # 35
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