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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation, Testing & Optimization | 16% | - A/B testing - System issue diagnosis - Evaluation framework design - Evaluation metrics and datasets - Cost and performance optimization - Production monitoring and optimization |
| Topic 2: Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - Developer enablement - Claude tooling configuration for teams - AI-assisted developer workflows |
| Topic 3: Solution Design & Architecture | 17% | - Translating business problems into Claude-based AI solutions - Architectural patterns
- Decomposition techniques for complex problem solving - End-to-end architecture design - Alignment with business value, cost, performance, and SLAs |
| Topic 4: Integration | 19% | - Enterprise system integration - Claude integration mechanisms
- RAG pipeline design
|
| Topic 5: Claude Models, Prompting & Context Engineering | 13% | - Claude model selection and trade-offs - Context window optimization - System prompts and prompt templates - Guardrails - Prompt reuse and context engineering strategies |
| Topic 6: Governance, Safety & Risk Management | 14% | - AI safety and guardrails - Security and risk management - Human-in-the-loop validation - Regulatory and compliance requirements - Ethical AI considerations |
| Topic 7: Stakeholder Communication & Lifecycle Management | 14% | - Communicating architectural decisions - Discovery and requirements gathering - Architecture documentation - Stakeholder management - Solution lifecycle management - Service-level agreements |
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NEW QUESTION # 102
You are classifying token-management tactics by where each tactic applies in the request lifecycle: "Input Preparation," "Prompt Construction," or "Output Handling."
Answer:
Explanation:
Explanation:
* Persist the validated output for downstream consumption and audit - Output Handling
* Order prompt sections so cacheable content appears before per-request content - Prompt Construction
* Summarize prior conversation history when full history is no longer needed - Input Preparation
* Validate the model's structured output against the expected schema - Output Handling
* Move stable repeated content into a cacheable prefix at the beginning of the prompt - Prompt Construction
* Trim retrieved passages to spans relevant to the user's question - Input Preparation Input preparation determines what evidence and history should enter the request, so summarization and retrieval trimming belong there. Prompt construction determines ordering and cache boundaries; stable repeated instructions must precede dynamic per-request content to maximize cache reuse. Output handling begins after generation and includes schema validation, persistence, auditing, and downstream delivery.
Mixing these responsibilities creates inefficient prompts and weak validation boundaries. Anthropic's context guidance emphasizes curating the smallest useful context because unnecessary tokens can reduce recall and accuracy. Prompt caching similarly depends on a stable reusable prefix, while structured outputs or schema validation protect downstream systems from malformed responses. Context windows ; prompt caching
NEW QUESTION # 103
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: B
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 # 104
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: B
NEW QUESTION # 105
You are running a risk assessment on a planned Claude-based deployment and must complete the inventory steps before assessing threats against assets.
Which two steps must be completed BEFORE assessing threats against assets to estimate likelihood and impact? (Select two.) Each correct answer presents part of the solution.
Answer: A,B
Explanation:
Threat assessment requires an inventory of what must be protected and who or what may threaten it. Asset identification should cover prompts, retrieved documents, credentials, model outputs, audit logs, tools, external systems, and sensitive business or personal data. Sensitivity classifications establish the potential confidentiality, integrity, safety, and availability impact of compromise. Threat actors and attack vectors must then be enumerated so likelihood and impact can be assessed against specific assets. Mitigation recommendations, residual-risk acceptance, ownership, and final documentation follow the analysis; they cannot be completed coherently before the threats are understood. Anthropic's security transparency materials similarly emphasize maintaining threat models that reflect relevant attacker tactics and techniques. Anthropic Transparency Hub
NEW QUESTION # 106
A technical team is debating whether to implement a new capability for generating regulatory filings from internal data as a Claude Skill or as an MCP server. The capability requires a fixed authoring procedure with embedded examples, does not need to call live external systems, and must be portable across three Claude deployments: claude.ai, an internal API integration, and Claude Code.
Which factor most strongly favors implementing the capability as a Skill?
Answer: B
Explanation:
A Skill is the appropriate packaging mechanism for reusable procedural knowledge, instructions, templates, examples, and supporting resources. The filing capability follows a defined authoring method and requires no live external-system interaction, so an MCP server would introduce unnecessary transport, authentication, hosting, and operational complexity. MCP becomes appropriate when Claude must invoke live databases, APIs, or enterprise services. Command-line availability does not independently determine the design because Skills can be used across supported Claude environments. Direct identity-provider authentication would favor an integration or MCP service rather than a self-contained Skill. Anthropic describes Agent Skills as portable packages that provide agents with specialized procedural and organizational expertise. Agent Skills
NEW QUESTION # 107
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