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| Section | Weight | Objectives |
|---|---|---|
| Governance, Safety & Risk Management | 14% | - Manage data privacy and security compliance - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Implement guardrails and safety controls - Address ethical AI considerations and bias mitigation |
| Claude Models, Prompting & Context Engineering | 13% | - Select appropriate Claude models based on trade-offs - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Design system prompts, templates, and guardrails |
| Stakeholder Communication & Lifecycle Management | 14% | - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs |
| Integration | 19% | - Integrate with data pipelines and RAG systems - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability - Integrate Claude with enterprise systems, APIs, and tools |
| Solution Design & Architecture | 17% | - Translate business problems into Claude-based AI solutions - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Design multi-agent systems and orchestration strategies - Design end-to-end architectures and feedback loops |
| 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% | - Optimize performance, prompting, and model selection - Define evaluation metrics and success criteria - Test accuracy, reliability, latency, and cost - Implement iterative improvement pipelines |
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NEW QUESTION # 81
You are responding to an adversarial input pattern in which users include text claiming admin authority and instructing the model to bypass safety restrictions.
Which combination of controls most effectively mitigates this attack pattern?
Answer: D
Explanation:
Self-declared administrative authority inside a prompt is untrusted data, not authenticated identity or authorization evidence. Option B correctly combines independent controls across the model, runtime, authorization, and monitoring layers.
Prompt instructions establish that user content cannot override system policy. Runtime classifiers detect known and generalized attempts to bypass controls. Tool permissions are enforced outside the model and must derive from authenticated identity, role, and approved scope-not statements contained in the conversation. Audit logging records the actor, attempted override, classifier result, tool requests, and final disposition for investigation and control improvement.
Anthropic recommends input screening, hardened system prompts, safe handling of untrusted content, narrowly scoped permissions, red-team testing, and continuous monitoring. Mitigate Jailbreaks and Prompt Injections Option A depends entirely on model behavior and provides no containment if the model fails. Option C removes the protections the attacker is attempting to defeat. Option D commits a fundamental authorization error by accepting an unverified claim as privilege elevation.
The strongest design also rate-limits repeated attempts, escalates suspicious activity, validates outputs, and requires human confirmation for consequential actions.
Study Guide references/topics: Direct prompt injection; untrusted user content; runtime classifiers; non-model authorization; scoped tools; audit logging; defense in depth.
NEW QUESTION # 82
You are configuring Claude Code for a team that needs every engineer to share the same MCP server set, permission rules, and project context across the engineering monorepo.
Which configuration scope best supports this requirement?
Answer: B
Explanation:
Project scope is designed for configuration that must be shared by everyone working in a repository.
Committing the approved configuration provides versioning, review history, repeatable onboarding, and consistent MCP and permission behavior across the monorepo. User scope is intended primarily for individual preferences and personal rules, so independently maintained user files create configuration drift. Local scope affects only one person in one project and is therefore unsuitable for team-wide distribution. A lead architect's local configuration cannot establish a shared environment. Claude Code documentation specifically describes .
claude/settings.json as the shared project settings file that teams check into source control. Security-critical policies that must not be overridden should additionally be enforced through managed organizational settings rather than relying exclusively on repository configuration. Claude Code: Settings files and precedence
NEW QUESTION # 83
A Claude-based research assistant begins producing responses that confidently contradict its retrieved source documents despite no change to the retrieval pipeline.
Which two diagnostic actions most directly identify the root cause of this behavior? (Select two.)
Answer: D,E
Explanation:
The retrieval pipeline is stated to be unchanged, so the first investigation should isolate changes in generation behavior and grounding instructions. Replaying the failing cases on the previous model version determines whether the regression follows the model upgrade or current model-prompt combination. Inspecting the system prompt establishes whether source-use rules, uncertainty behavior, citation requirements, or prohibitions against unsupported claims were removed or weakened. Increasing context or replacing the embedding model changes a component for which no failure evidence exists and may introduce additional noise. Lowering temperature may reduce variation but does not explain systematic contradiction of retrieved evidence. The investigation should compare identical requests, retrieved passages, prompts, and model versions before applying a fix. Define success criteria and evaluations
NEW QUESTION # 84
A pilot AI assistant for procurement specialists shows 89 percent first-response acceptance, but follow-up surveys reveal that specialists frequently override the assistant's vendor recommendations after considering criteria the assistant did not evaluate. The pilot owner wants to ship the assistant unchanged because of the strong acceptance rate.
Which two Discernment-competency observations should you raise BEFORE approving the launch? (Select two.)
Answer: B,E
Explanation:
The assistant is making recommendations without considering information that specialists regard as material.
That is a direct input-scope and requirements gap, not merely a presentation issue. The 89 percent acceptance metric is also incomplete because initial acceptance does not reveal whether recommendations remain correct after users apply the omitted criteria. Production readiness requires outcome-focused evaluation, including final recommendation agreement, override reasons, financial impact, supplier risk, and performance across representative procurement scenarios. Options B and E raise possible concerns, but the scenario supplies no evidence that response rate or pilot duration is the demonstrated deficiency. Option A incorrectly treats a single proxy metric as sufficient. Anthropic recommends multidimensional, task-specific success criteria aligned with the actual user outcome. Define success criteria and evaluations
NEW QUESTION # 85
You are evaluating model-selection claims used by a peer team.
For each statement, select yes if the statement is generally true. Otherwise, select no.
Answer:
Explanation:
Explanation:
Yes, Yes, No, Yes, No
More capable reasoning configurations generally require greater computation, produce higher latency, and may consume larger token or rate-limit allocations than lighter alternatives. This makes the first statement generally accurate, although actual performance must be measured under the organization's specific provider and service tier.
Representative workload testing is essential because generic benchmark rankings do not establish performance on the organization's prompts, data, edge cases, tools, or required output format. Anthropic recommends benchmark tests specific to the use case, using actual prompts and data, followed by comparison of accuracy, quality, edge-case behavior, and cost. Choosing the Right Model The strongest model is not automatically the correct production choice. Model selection must balance capability, speed, cost, throughput, and operational constraints. Tiered routing implements this principle by serving predictable requests with an efficient model and escalating difficult cases to a more capable model.
Anthropic documents multi-model strategies in which lower-cost workers handle most traffic while harder decisions are escalated.
Recency is also not a valid selection criterion. A newly announced model must pass the workload's existing evaluation, security, latency, and cost gates before replacing a validated production model.
Study Guide references/topics: Model trade-offs; representative evaluations; tiered routing; escalation architectures; cost-capability optimization; model migration validation.
NEW QUESTION # 86
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