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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Manage stakeholder feedback and expectation alignment - Conduct structured discovery and requirement gathering - Communicate architectural decisions and trade-offs |
| Topic 2: Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Design end-to-end architectures and feedback loops - Translate business problems into Claude-based AI solutions - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars |
| Topic 3: Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Apply context engineering and context management techniques - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails |
| Topic 4: 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.) |
| Topic 5: 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 |
| Topic 6: 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 |
| Topic 7: Integration | 19% | - Integrate with data pipelines and RAG systems - Integrate Claude with enterprise systems, APIs, and tools - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability |
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NEW QUESTION # 53
You are reviewing a peer's end-to-end design for a Claude-based platform expected to scale to thousands of concurrent users.
For each statement, indicate Yes if it reflects sound architectural practice. Otherwise, select No.
Answer:
Explanation:
Explanation:
* Authentication and authorization run before retrieval so retrieval can filter by identity - Yes
* An asynchronous queue absorbs bursty traffic between intake and the model-invocation layer - Yes
* Tax computation is encoded directly in the system prompt rather than in code - No
* Conversation logs include unredacted government identifiers to maximize tuning signal - No Authentication and authorization must precede retrieval so unauthorized information never enters model context. An asynchronous queue is appropriate for burst absorption, backpressure, controlled concurrency, and retry management at scale. Deterministic tax computation should be implemented in validated code or a controlled calculation tool, not delegated to probabilistic prompt interpretation. Including unredacted government identifiers in conversation logs violates data-minimization principles and creates unnecessary privacy, security, and regulatory exposure. Sensitive fields should be removed, tokenized, or redacted before logging or model use unless specifically required and authorized. The resulting design separates deterministic computation, access control, scalable orchestration, and language reasoning into appropriate architectural layers.
NEW QUESTION # 54
You are rolling out monitoring for a Claude-based deployment and must complete the specification steps before instrumenting the deployment.
Which two steps must be completed BEFORE instrumenting the deployment to emit metrics and traces?
(Select two.)
Each correct answer presents part of the solution.
Answer: C,E
NEW QUESTION # 55
A loan pre-qualification assistant shows 94 percent approval recommendations that match the human underwriter decision. The fairness team has reviewed approval rate parity across protected groups and reported no significant difference. A board member has asked whether this evidence is sufficient to declare the assistant fair.
Which two Discernment-competency findings should you report? (Select two.) Each correct answer presents part of the solution.
Answer: A,E
Explanation:
Approval-rate parity measures whether groups receive positive recommendations at similar rates. It does not establish whether false approvals, false denials, sensitivity, specificity, or calibration are comparable across those groups. A system can therefore satisfy aggregate approval parity while imposing materially different error burdens on protected populations. The evaluation must include subgroup confusion matrices, false- positive and false-negative rates, calibration, intersectional analysis, and confidence intervals.
The 94 percent agreement rate measures fidelity to human underwriter decisions, not fairness. Human decisions are not automatically unbiased ground truth. If historical underwriting practices contain structural, procedural, or measurement bias, a model that reproduces those decisions accurately can reproduce the same bias. The reference labels must therefore be independently assessed for legitimacy, consistency, and potential discriminatory effects.
Nothing in the scenario establishes that protected groups were omitted, so D is unsupported. Similarly, sample size may require examination, but the scenario provides no statistical information proving that sample size is the principal deficiency. The evidence already contains two identifiable conceptual gaps regardless of sample size.
Study Guide references/topics: [Defining multidimensional evaluation criteria](https://docs.anthropic.com/en
/docs/build-with-claude/develop-tests); fairness measurement; subgroup error analysis; label and benchmark bias; human-baseline limitations; governance evidence.
NEW QUESTION # 56
You are integrating human review into a high-volume classification pipeline where reviewing every output is infeasible.
Which sampling strategy best balances throughput with quality oversight?
Answer: D
Explanation:
Risk-stratified sampling concentrates limited reviewer capacity where error consequences and uncertainty are greatest. All low-confidence and high-impact cases should receive mandatory review, while random sampling of routine high-confidence outputs provides an unbiased signal for drift, unexpected failure modes, and overconfident errors. Complaint-only monitoring discovers defects after harm occurs. Reviewing only high- confidence cases systematically ignores the most dangerous outputs. Universal review provides maximum coverage but contradicts the stated throughput constraint and may create queues that undermine service objectives. The sampling policy should define confidence calibration, impact categories, escalation thresholds, review SLAs, and periodic adjustment based on observed error rates. Reviewer decisions should feed the evaluation dataset so monitoring quality improves over time.
NEW QUESTION # 57
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 # 58
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