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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Claude Models, Prompting & Context Engineering | 13% | - Prompt reuse and context engineering strategies - Claude model selection and trade-offs - Context window optimization - System prompts and prompt templates - Guardrails |
| Topic 2: Stakeholder Communication & Lifecycle Management | 14% | - Architecture documentation - Solution lifecycle management - Communicating architectural decisions - Stakeholder management - Discovery and requirements gathering - Service-level agreements |
| Topic 3: Developer Productivity & Operational Enablement | 7% | - AI-assisted developer workflows - Debugging and operational issue resolution - Claude tooling configuration for teams - Developer enablement |
| Topic 4: Governance, Safety & Risk Management | 14% | - Ethical AI considerations - Human-in-the-loop validation - Regulatory and compliance requirements - Security and risk management - AI safety and guardrails |
| Topic 5: Evaluation, Testing & Optimization | 16% | - Cost and performance optimization - Evaluation framework design - Evaluation metrics and datasets - System issue diagnosis - Production monitoring and optimization - A/B testing |
| Topic 6: Solution Design & Architecture | 17% | - End-to-end architecture design - Multi-agent systems and orchestration - Translating business problems into Claude-based AI solutions - Alignment with business value, cost, performance, and SLAs - Architectural patterns
|
| Topic 7: Integration | 19% | - Authentication and authorization analysis - Enterprise system integration - RAG pipeline design
|
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NEW QUESTION # 76
You are a solution architect designing a Claude-based assistant with access to 60 internal tools across multiple business domains. Loading every tool definition on every request increases token usage and time to first response.
Which design pattern best addresses this issue without sacrificing capability breadth?
Answer: C
Explanation:
Progressive tool discovery preserves the complete capability catalog while limiting initial context to a curated set of common common common tools and a discovery mechanism. When the task requires an additional capability, Claude searches the catalog and loads only the relevant tool definitions.
Anthropic's Tool Search mechanism implements implements this pattern through deferred loading. The context initially contains the search search search tool and any deliberately non-deferred tools; relevant definitions are expanded only when discovered. This reduces token consumption and helps maintain tool- selection accuracy in large catalogs. ToolTool Search Tool Option B adds a model call and still repeatedly processes all definitions, increasing latency latency latency and cost while potentially losing important schema details. Option C treats a larger context window as permission to waste context and does not address time-to-first-response overhead. Option D reduces capability breadth because the fixed five tools may be irrelevant to a given task.
The architect should provide clear tool and server server server descriptions, define which high-frequency tools remain immediately available, and evaluate discovery recall, selection accuracy
NEW QUESTION # 77
You are identifying signals that a deployment should re-enter design rather than continue iterating in place.
Which signal most directly indicates the need for a new design cycle?
Answer: A
Explanation:
A new design cycle is warranted when the required change crosses an architectural boundary. Option D explicitly states that the existing architecture cannot satisfy the new requirements without redistributing component responsibilities or altering core contracts. Such changes may affect service ownership, orchestration, data flows, integration interfaces, security boundaries, failure handling, evaluation strategy, and operational accountability. They therefore require renewed discovery, impact analysis, design review, stakeholder approval, and regression planning.
Options A, B, and C are localized maintenance activities. Clarifying a runbook improves operational documentation but does not change the deployed system's structure. Adjusting an alert threshold is a controlled operational tuning activity, provided the change remains within established monitoring policy.
Editing customer-facing copy is similarly confined to the presentation or content layer. None of these changes inherently invalidates component contracts or architectural assumptions.
An architect should distinguish between iteration within an approved design and evidence that the design itself no longer supports the required outcome. Re-entering design for every minor adjustment creates unnecessary governance overhead; continuing local iteration after architectural assumptions have failed creates unmanaged technical and operational risk. Option D is the only signal that establishes a structural incompatibility.
Study Guide references/topics: Lifecycle feedback loops; design re-entry criteria; architectural significance; component responsibilities; interface contracts; controlled operational iteration.
NEW QUESTION # 78
You are a solution architect evaluating candidate use cases for a Claude-based program.
For each scenario, select Yes if Claude is appropriate as the primary solution at the architectural level.
Otherwise, select No.
Answer:
Explanation:
Explanation:
* Drafting first-pass investigative reports from semi-structured incident logs for analyst review - Yes
* Computing real-time fraud scores at sub-50-millisecond latency on a streaming pipeline - No
* Long-context contract review with structured clause extraction and deviation flagging - Yes
* Replacing a vector index for semantic retrieval over a multi-million-document corpus - No
* Routing inbound support tickets into 30 categories with reasoning for the selected route - Yes Claude is well suited to language-intensive analysis, summarization, extraction, classification, and drafting.
Investigative-report drafting, contract analysis, and support-ticket routing all require interpretation of unstructured or semi-structured language and can incorporate human review. A sub-50-millisecond fraud- scoring path requires deterministic, specialized real-time processing and should not place LLM inference on the critical path. Claude also does not replace the retrieval index used to search millions of documents; it consumes the results produced by that infrastructure. The correct architectural decision separates tasks requiring semantic language reasoning from deterministic computation and information-retrieval infrastructure. Model selection and evaluation must then confirm that the chosen Claude model satisfies quality, latency, and cost requirements.
NEW QUESTION # 79
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: C
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 # 80
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 # 81
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