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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluation, Testing & Optimization | 16% | - Evaluation framework design - Cost and performance optimization - Production monitoring and optimization - Evaluation metrics and datasets - System issue diagnosis - A/B testing |
| Topic 2: Solution Design & Architecture | 17% | - Decomposition techniques for complex problem solving - Multi-agent systems and orchestration - End-to-end architecture design - Translating business problems into Claude-based AI solutions - Architectural patterns
|
| Topic 3: Governance, Safety & Risk Management | 14% | - Ethical AI considerations - Regulatory and compliance requirements - Human-in-the-loop validation - Security and risk management - AI safety and guardrails |
| Topic 4: Integration | 19% | - Authentication and authorization analysis - Enterprise system integration - RAG pipeline design
|
| Topic 5: Stakeholder Communication & Lifecycle Management | 14% | - Service-level agreements - Discovery and requirements gathering - Solution lifecycle management - Stakeholder management - Architecture documentation - Communicating architectural decisions |
| Topic 6: Developer Productivity & Operational Enablement | 7% | - AI-assisted developer workflows - Developer enablement - Claude tooling configuration for teams - Debugging and operational issue resolution |
| Topic 7: Claude Models, Prompting & Context Engineering | 13% | - Claude model selection and trade-offs - System prompts and prompt templates - Prompt reuse and context engineering strategies - Context window optimization - Guardrails |
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NEW QUESTION # 74
An operations engineer reports that a Claude-based pipeline began returning malformed JSON responses after a scheduled maintenance window, causing downstream processing failures.
Which two investigative steps most directly isolate the root cause? (Select two.)
Answer: A,B
Explanation:
The strongest investigation combines configuration comparison with controlled replay. Comparing the current system prompt and output-schema settings against the last known good version identifies maintenance-related configuration drift. Relevant differences include removed formatting instructions, altered schema requirements, incompatible parser expectations, changed stop sequences, or a disabled structured-output mechanism.
Replaying known premaintenance requests against the current configuration creates a controlled test set.
Inspecting the raw response before parsing then establishes whether malformed JSON originates in model generation or is introduced by streaming assembly, transport handling, post-processing, or the downstream parser. If the raw response is valid but the pipeline rejects it, the fault lies after generation. If the raw response is malformed, the prompt, schema enforcement, stop reason, and request configuration become the primary investigation targets.
Clearing the prompt cache assumes cache corruption without evidence. Changing model tiers introduces another variable and weakens causal attribution. Raising `max_tokens` is appropriate only when raw responses show truncation and the returned stop reason indicates the token limit was reached; it is not the most direct initial test.
Study Guide references/topics: [Increasing output consistency](https://docs.anthropic.com/en/docs/test-and- evaluate/strengthen-guardrails/increase-consistency); [agent evaluation methodology] (https://www.anthropic.
com/engineering/demystifying-evals-for-ai-agents); regression replay; last-known-good comparison; raw- output inspection.
NEW QUESTION # 75
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: A
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 # 76
You are reviewing an integration specification for security gaps.
Which two findings constitute valid security gaps in the specification? (Select two.) Each correct answer presents a complete solution.
Answer: A,D
Explanation:
Credentials must never be supplied as prompt content. Prompts can be retained in traces, debugging output, caches, or logs, and model output may inadvertently expose included secrets. Credentials should instead be resolved server-side through an approved secret store or delegated authentication mechanism. Option C is also a serious gap because post-response RBAC occurs after unauthorized data has already entered model context.
Authorization must be enforced before retrieval and prompt construction. Options A, D, and E describe appropriate controls: least-privilege service identities, per-user scoped OAuth, and encrypted transport.
Anthropic's prompt-leak guidance recommends excluding unnecessary sensitive information, while Claude Code security relies on permission-based enforcement rather than natural-language instructions alone. Reduce prompt leak
NEW QUESTION # 77
When communicating an architectural decision to a security and compliance reviewer, which content set is most aligned with that audience's primary concerns?
Answer: B
Explanation:
Security and compliance reviewers must determine whether material threats have been identified, whether required controls address those threats, how remaining exposure will be accepted, and whether the decision can be demonstrated during an audit. Option C supplies exactly that evidence.
The threat model establishes protected assets, trust boundaries, adversaries, attack paths, and likely failure modes. Control mappings connect technical and procedural safeguards to organizational policies, regulatory requirements, and assurance frameworks. Residual-risk acceptance criteria define which risks may remain after mitigation, who has authority to accept them, and when further treatment is mandatory. Audit traceability connects requirements and decisions to implementation evidence, evaluations, approvals, logs, and operational monitoring.
Option A is designed primarily for product management. Option B addresses engineering planning and execution. Option D is appropriate for an executive sponsor who requires business justification and a concise risk summary but normally does not perform detailed control verification.
Audience adaptation should not alter the architectural decision itself. It changes the depth, terminology, evidence, and ordering used to communicate the decision. Security and compliance reviewers require verifiable control evidence rather than only schedule, implementation, or business-level summaries.
Study Guide references/topics: Audience-specific communication; threat modeling; compliance control mapping; residual-risk acceptance; audit evidence; architectural decision traceability.
NEW QUESTION # 78
An engineering organization is adopting Claude Code across 200 developers. A team lead proposes that AI- generated pull requests bypass standard code review for changes under 50 lines because small changes are considered low risk and review capacity is constrained.
Which two Diligence-competency objections should you raise? (Select two.) Each correct answer presents part of the solution.
Answer: D,E
Explanation:
AI-generated code requires verification through the same controlled engineering workflow as human-authored changes. Removing review eliminates the gate that can detect incorrect assumptions, security defects, missing tests, and unintended behavior. Line count is not a defensible risk measure: a one-line authorization change, dependency alteration, or command-execution defect can be more consequential than a large documentation update. Review requirements should instead be determined by affected components, privilege, data sensitivity, reversibility, test coverage, and deployment impact. Expanding capacity may be operationally useful but is not the central diligence objection. Raising the threshold retains the same flawed proxy, while rejecting all AI-generated changes discards legitimate productivity benefits. Anthropic states that users remain responsible for reviewing proposed code and commands. Claude Code security
NEW QUESTION # 79
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