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| Section | Weight | Objectives |
|---|---|---|
| Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection |
| Claude Models, Prompting & Context Engineering | 13% | - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques |
| Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering |
| Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Manage data privacy and security compliance |
| Integration | 19% | - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools - Implement Model Context Protocol (MCP) integrations |
| Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Design end-to-end architectures and feedback loops - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions |
| 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 # 107
You are listing characteristics of strong architectural-decision communication.
Which two characteristics belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: B,C
Explanation:
Strong decision communication must explain both how the decision was reached and how it will remain governed. Option D provides the reasoning structure: the alternatives considered and the criteria used to compare them. This allows reviewers to verify that capability, cost, latency, security, maintainability, integration complexity, and business outcomes were evaluated consistently rather than selectively.
Option A establishes lifecycle accountability. Architectural decisions depend on assumptions about requirements, models, data, regulations, usage volume, and operational constraints. Naming an owner and review cadence ensures the decision is reconsidered when those assumptions materially change.
Option B imposes a meeting mechanism rather than describing decision quality. Mandatory attendance by senior leaders may add delay without improving the clarity or correctness of the record. Option C removes important evidence and increases the chance that rejected approaches will be proposed again without addressing the original objections. Option E can improve consistency and discoverability, but visual standardization alone does not establish substantive architectural communication.
A strong decision record should therefore be evidence-based, audience-accessible, traceable to requirements, explicit about alternatives and consequences, assigned to an accountable owner, and governed by defined review triggers.
Study Guide references/topics: Architectural-decision communication; alternatives analysis; evaluation criteria; decision ownership; review cadence; changing assumptions; lifecycle governance.
NEW QUESTION # 108
A research summarization assistant has been deployed for six months. A user has flagged that a generated summary contained a fabricated citation. The product team has asked whether the incident requires architectural action or whether it is an isolated case.
Which two Diligence-competency actions should you take? (Select two.)
Each correct answer presents part of the solution.
Answer: A,E
Explanation:
A single confirmed fabrication requires investigation, but it does not establish the population-level failure rate. Sampling recent summaries determines prevalence, affected content types, and whether the incident correlates with particular prompts, documents, or model versions. The architecture must also be examined for grounding controls, including source-constrained generation, citation rendering, claim-to-source verification, and rejection of unsupported statements. Anthropic recommends making factual outputs auditable through citations and requiring supporting quotations for important claims. Immediate universal shutdown may become necessary if the risk is severe, but doing so before establishing scope is not the most disciplined first response. Options D and E dismiss evidence without validation and would prevent corrective action.
Reducing hallucinations
NEW QUESTION # 109
You are selecting a protocol for a single low-latency stateless tool call from a Claude-based assistant to an internal pricing service that already exposes a stable HTTP API.
Which integration mechanism is the most appropriate?
Answer: D
Explanation:
A direct call to the existing stable HTTP endpoint is the simplest mechanism that satisfies the stated requirements. The interaction is stateless, requires only one tool invocation, and has a strict latency objective.
Adding session management, message-bus translation, or another model-mediated agent would introduce unnecessary network hops, operational dependencies, failure modes, and processing latency.
The assistant's tool implementation should validate input parameters, authenticate through a server-side credential mechanism, apply narrowly scoped authorization, set explicit timeouts, and validate the pricing response before returning it to Claude. Credentials must never be supplied by or exposed to the model.
Appropriate logging should capture request attribution, endpoint outcome, and timing without unnecessarily recording sensitive data.
Anthropic describes the Claude API as a RESTful interface and supports custom tools in which the application executes the requested function or API operation. Claude API Overview , Tool Use Overview MCP could be justified when standardized discovery or reuse across clients is required, but the scenario already supplies a stable API and does not establish that additional requirement.
Study Guide references/topics: Protocol selection; direct API integration; stateless calls; latency minimization; scoped credentials; avoiding unnecessary agentic complexity.
NEW QUESTION # 110
You are defining escalation criteria for ambiguous cases in an automated workflow.
Which set of criteria best supports consistent escalation?
Answer: B
Explanation:
Option D defines observable and repeatable escalation triggers. Confidence thresholds identify cases where the system lacks sufficient certainty. High-impact categories ensure that legally, financially, medically, or operationally consequential decisions receive human attention regardless of nominal confidence. Classifier- generated ambiguity flags add an independent control, while an explicit user request preserves the individual' s ability to obtain review. These triggers should be documented, tested, logged, and mapped to reviewer authority and response-time objectives. Options A and C make escalation discretionary and inconsistent.
Option B is purely reactive because the potentially harmful automated action has already occurred. A sound workflow escalates before consequential execution and records the reason, evidence, reviewer, decision, and outcome.
Study Guide references/topics: Human-in-the-loop governance; escalation thresholds; ambiguity detection; consequential decisions; review auditability.
NEW QUESTION # 111
You are classifying chunking strategies by the corpus type each is best suited to.
For each chunking strategy, select the appropriate corpus type: "Long Structured Documents,"
"Heterogeneous Short Records," or "Code or Hierarchical Specifications."
Answer:
Explanation:
Explanation:
* Function-level or section-level chunking for code modules - Code or Hierarchical Specifications
* Tree-aware chunking that follows code or specification hierarchy - Code or Hierarchical Specifications
* Per-record chunking where each record is one chunk - Heterogeneous Short Records
* Semantic chunking along clause or paragraph boundaries - Long Structured Documents
* Fixed-size chunking with overlap for short records of similar length - Heterogeneous Short Records
* Hierarchical chunking that mirrors document section structure - Long Structured Documents Chunking must preserve the structural unit that carries meaning in the source corpus. Code and hierarchical specifications are best divided at function, module, class, or tree boundaries because arbitrary token cuts can separate definitions from their implementation or parent context. Heterogeneous short-record collections should generally preserve each record as an independent chunk. Fixed-size overlapping chunks are also effective when records have broadly similar lengths and lack meaningful internal hierarchy. Long structured documents benefit from semantic boundaries such as clauses and paragraphs, while hierarchical chunking preserves relationships among sections, subsections, and parent headings. Anthropic notes that chunk size, boundaries, and overlap materially affect retrieval performance; therefore, one universal chunking method is inappropriate. Anthropic Contextual Retrieval
NEW QUESTION # 112
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