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| Section | Weight | Objectives |
|---|---|---|
| Solution Design & Architecture | 17% | - Translate business problems into Claude-based AI solutions - Align solutions to business value pillars - Select architectural patterns: workflow, agentic, augmented LLM - Design multi-agent systems and orchestration strategies - Design end-to-end architectures and feedback loops |
| Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Design system prompts, templates, and guardrails - Select appropriate Claude models based on trade-offs - Apply context engineering and context management techniques |
| Developer Productivity & Operational Enablement | 7% | - Support debugging, monitoring, and operational resolution - Improve developer workflows with AI-assisted tooling - Configure Claude tools and environments for teams |
| Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Test accuracy, reliability, latency, and cost - Define evaluation metrics and success criteria - Optimize performance, prompting, and model selection |
| Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) |
| Integration | 19% | - Design authentication, authorization, and observability - Implement Model Context Protocol (MCP) integrations - Integrate Claude with enterprise systems, APIs, and tools - Integrate with data pipelines and RAG systems |
| Stakeholder Communication & Lifecycle Management | 14% | - Communicate architectural decisions and trade-offs - Document architectures and support full lifecycle phases - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering |
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NEW QUESTION # 29
You are compiling guardrail tactics for a customer-facing assistant.
Which two tactics belong on the guardrail list?
Each correct answer presents a complete solution.
Answer: A,E
Explanation:
Option C introduces a deterministic validation boundary between probabilistic model output and downstream execution. Structured validation can reject missing fields, invalid types, unauthorized actions, malformed arguments, and values outside approved enumerations before any tool or business process acts on the response. Anthropic recommends Structured Outputs when guaranteed schema conformance is required.
Increase Output Consistency
Option D implements defense in depth. Prompt-level instructions establish intended behavior, while runtime classifiers, content filters, policy engines, output validators, tool permissions, and human confirmation gates provide controls outside the model. Anthropic's guardrail guidance recommends combining safeguards rather than depending on one mechanism. Mitigate Jailbreaks and Prompt Injections Option A creates a bypass mechanism that adversaries may discover or imitate. Option B may alter output variability but is not a security boundary and cannot guarantee policy compliance. Option E places complete reliance on a system prompt, which remains vulnerable to ambiguity, conflicting inputs, model errors, and indirect prompt injection.
Guardrails should be risk-based, independently enforceable where possible, observable, tested against adversarial cases, and proportional to the consequences of downstream actions.
Study Guide references/topics: Layered guardrails; structured validation; runtime content checks; prompt injection; deterministic enforcement; defense in depth.
NEW QUESTION # 30
A security team is evaluating two proposed controls. Control A adds an outbound tool allow-list with destination restrictions and per-call review. Control B scores responses against a stable adversarial evaluation set after each model-version change.
Which two risk categories are correctly matched to these controls? (Select two.)
Answer: C,E
Explanation:
Control A directly constrains the outbound action surface. An allow-list limits which tools and destinations may receive data, while per-call review introduces an approval boundary before information leaves the controlled environment. These measures therefore address data exfiltration through outbound tool calls, making Option C correct. Anthropic's security guidance recommends least privilege, narrowly scoped permissions, sandboxing, and limiting access to sensitive information and actions so that a compromised agent can cause minimal damage. Mitigate Jailbreaks and Prompt Injections Control B addresses model-version drift. Running a stable adversarial evaluation set after each version change creates a consistent comparison baseline and detects regressions that might otherwise remain invisible during routine testing. This correctly maps Control B to silent quality drift, making Option E correct. Anthropic's migration guidance repeatedly calls for workload reevaluation because instruction following, style, reasoning, and agent behavior can differ between versions. Model Migration Guide Control A may limit the consequences of successful prompt injection, but it does not prevent or directly detect adversarial instructions in retrieved content. Control B measures behavioral performance but does not impose outbound authorization controls. Each control must therefore be mapped to the risk it most directly mitigates.
Study Guide references/topics: Tool governance; least privilege; exfiltration controls; adversarial evaluations; model-change regression testing; defense in depth.
NEW QUESTION # 31
You are listing characteristics of robust guardrail design for an enterprise deployment.
Which two characteristics belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: C,E
NEW QUESTION # 32
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 # 33
You are reviewing a customer-support agent's configuration. Each candidate tool falls into one of four categories: (1) required to complete defined tasks, (2) frequently used and reduces hand-offs, (3) occasionally useful for unrelated work, (4) speculative future utility.
Which categories should typically remain in the agent configuration?
Answer: D
Explanation:
Category 1 tools are necessary for the agent to complete its approved responsibilities and must remain.
Category 2 tools are also justified when they are regularly used and eliminate predictable hand-offs without expanding the agent beyond its defined operating role.
Categories 3 and 4 create capability bloat. A tool that is occasionally useful only for unrelated work does not belong to this agent's responsibility boundary. A speculative tool has no validated requirement and adds attack surface, context consumption, authorization complexity, evaluation burden, and operational dependencies without demonstrated value.
Tool inclusion should be based on task traceability: every configured capability should map to a documented user journey, responsibility, permission scope, and evaluation case. Anthropic recommends least privilege so that a successful injection or model error can cause minimal damage. Mitigate Jailbreaks and Prompt Injections The architect should periodically review tool-call telemetry and remove unused capabilities. New tools can be added when a validated workflow requires them, after security review and evaluation. The objective is not the smallest possible catalog regardless of usefulness; it is the smallest catalog that reliably completes the agent's approved tasks.
Study Guide references/topics: Capability-bloat analysis; tool necessity; hand-off reduction; least privilege; attack-surface management; tool lifecycle review.
NEW QUESTION # 34
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