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| Section | Weight | Objectives |
|---|---|---|
| Solution Design & Architecture | 17% | - End-to-end architecture design - Multi-agent systems and orchestration - Decomposition techniques for complex problem solving - Translating business problems into Claude-based AI solutions - Architectural patterns
|
| Evaluation, Testing & Optimization | 16% | - Evaluation metrics and datasets - Cost and performance optimization - System issue diagnosis - Production monitoring and optimization - A/B testing - Evaluation framework design |
| Integration | 19% | - Claude integration mechanisms
- Enterprise system integration |
| Governance, Safety & Risk Management | 14% | - Security and risk management - Ethical AI considerations - AI safety and guardrails - Human-in-the-loop validation - Regulatory and compliance requirements |
| Developer Productivity & Operational Enablement | 7% | - AI-assisted developer workflows - Claude tooling configuration for teams - Developer enablement - Debugging and operational issue resolution |
| Stakeholder Communication & Lifecycle Management | 14% | - Communicating architectural decisions - Stakeholder management - Service-level agreements - Solution lifecycle management - Architecture documentation - Discovery and requirements gathering |
| Claude Models, Prompting & Context Engineering | 13% | - System prompts and prompt templates - Claude model selection and trade-offs - Guardrails - Context window optimization - Prompt reuse and context engineering strategies |
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NEW QUESTION # 123
You are identifying the highest-impact optimization for a deployment whose token cost is dominated by a long, repeated system prompt and a large retrieved context per request.
Which optimization most directly targets the dominant cost driver?
Answer: A
NEW QUESTION # 124
A technical team is cataloguing risks specific to Claude's use in a document-grounded Q & A system.
Which two items represent failure modes intrinsic to LLM-based systems rather than generic software defects? (Select two.)
Answer: C,D
NEW QUESTION # 125
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 # 126
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 # 127
A customer support team has proposed delegating customer refund decisions to a Claude-driven workflow with no human review for refunds under 50 USD. The team's reasoning is that small refunds are low-risk and human review would erase the efficiency gain.
Which Delegation-competency principle should guide your response?
Answer: A
Explanation:
Transaction value is only one component of risk. Even a small refund can create fraud exposure, discriminatory outcomes, contractual violations, account compromise, policy inconsistency, or cumulative financial loss. Delegation should therefore depend on the nature, detectability, reversibility, and aggregate impact of failure-not a monetary threshold alone. Option B supports risk-stratified automation: routine, well- evidenced cases may proceed automatically, while anomalous, identity-sensitive, disputed, or policy-edge cases require review or escalation. Anthropic's trustworthy-agent principles emphasize maintaining meaningful human control rather than imposing universal review or unrestricted autonomy. Option A is unnecessarily absolute; Option C prioritizes efficiency over governance; and Option D prohibits potentially safe, controlled automation without risk analysis.
Study Guide references/topics: Delegation competency; human control; risk classification; reversibility; cumulative exposure; escalation design.
NEW QUESTION # 128
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