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| Section | Weight | Objectives |
|---|---|---|
| Governance, Safety & Risk Management | 14% | - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Manage data privacy and security compliance - Address ethical AI considerations and bias mitigation |
| Claude Models, Prompting & Context Engineering | 13% | - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques - Mitigate prompt injection, leaks, and jailbreak risks |
| Evaluation, Testing & Optimization | 16% | - Test accuracy, reliability, latency, and cost - Optimize performance, prompting, and model selection - Implement iterative improvement pipelines - Define evaluation metrics and success criteria |
| Integration | 19% | - Integrate Claude with enterprise systems, APIs, and tools - Implement Model Context Protocol (MCP) integrations - Design authentication, authorization, and observability - Integrate with data pipelines and RAG systems |
| Solution Design & Architecture | 17% | - Design multi-agent systems and orchestration strategies - Align solutions to business value pillars - Translate business problems into Claude-based AI solutions - Select architectural patterns: workflow, agentic, augmented LLM - Design end-to-end architectures and feedback loops |
| Stakeholder Communication & Lifecycle Management | 14% | - Conduct structured discovery and requirement gathering - Manage stakeholder feedback and expectation alignment - Document architectures and support full lifecycle phases - Communicate architectural decisions and trade-offs |
| Developer Productivity & Operational Enablement | 7% | - Improve developer workflows with AI-assisted tooling - Support debugging, monitoring, and operational resolution - Configure Claude tools and environments for teams |
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NEW QUESTION # 13
You are comparing patterns for a batch document-classification job that follows fixed steps: extract metadata, classify, summarize, and persist.
Which pattern is the best fit and why?
Answer: D
Explanation:
A workflow is appropriate when processing stages and transitions are known in advance. Metadata extraction, classification, summarization, and persistence can be represented as deterministic nodes with explicit schemas, validation rules, retry policies, and failure handling. This provides predictable execution, cost, observability, and testability without paying for repeated model-driven planning. Anthropic's Building Effective AI Agents distinguishes workflows, where predefined code paths control execution, from agents, where the model dynamically determines its process. Option A reaches the correct pattern for an incorrect reason: workflows and agents can both invoke tools. Options B and C introduce unnecessary autonomy and make unsupported claims about consistency or universal accuracy.
Study Guide references/topics: Workflow versus agentic patterns; deterministic orchestration; batch processing; structured outputs; predictable cost; error handling.
NEW QUESTION # 14
You are designing the prompt for a ticket-triage classifier with thirty well-defined categories and clear category descriptions in the prompt.
Which technique is most appropriate as the starting point?
Answer: B
Explanation:
A clearly defined closed-set classification task should begin with a concise zero-shot prompt containing the permitted categories, unambiguous category descriptions, and the required output structure. This provides a simple, inexpensive baseline that can be measured against a representative evaluation set. Chain-of-thought prompting adds unnecessary latency and token usage when the routing decision does not require substantive multi-step reasoning. Permitting Claude to invent categories would violate the closed-set contract and complicate downstream automation. Omitting descriptions would increase ambiguity among potentially overlapping labels. If evaluation later reveals persistent confusion between particular categories, the architect can introduce targeted examples or refine the definitions. Prompt complexity should be added in response to measured failure patterns, not assumed to be necessary before establishing baseline performance. Anthropic:
Prompt engineering overview
NEW QUESTION # 15
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: D,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 # 16
You are building an evaluation pipeline for a Claude-based deployment and must complete the specification steps before running the deployment against the dataset.
Which two steps must be completed BEFORE running the deployment against the evaluation dataset? (Select two.) Each correct answer presents part of the solution.
Answer: A,B
Explanation:
The evaluation specification must exist before inference begins. Option D defines what the evaluation will measure and how results will be segmented. Representative cases measure normal workload performance, edge cases test boundary behavior, and adversarial cases examine safety, security, or robustness under hostile inputs.
Option B then supplies the correctly curated and labeled dataset corresponding to those slices. Labels, expected outcomes, grading rubrics, provenance, and dataset versions must be established before the deployment generates outputs. Otherwise, evaluators risk modifying criteria after seeing results and introducing confirmation bias.
Anthropic defines an evaluation as an input combined with grading logic used to measure success. Its evaluation guidance distinguishes tasks, trials, graders, traces, outcomes, and the harness that runs and aggregates them. Demystifying Evals for AI Agents Option E occurs after model execution because outputs must exist before they can be scored. Option A follows scoring and aggregation. Option C is an iterative improvement step performed after failure cases become available, although rubric calibration may also be conducted on separate development data.
Study Guide references/topics: Evaluation specification; metrics and slices; representative and adversarial datasets; labeling; scoring; aggregation; release gates.
NEW QUESTION # 17
You are running a risk assessment on a planned Claude-based deployment and must complete the inventory steps before assessing threats against assets.
Which two steps must be completed BEFORE assessing threats against assets to estimate likelihood and impact? (Select two.) Each correct answer presents part of the solution.
Answer: C,E
Explanation:
Threat assessment requires an inventory of what must be protected and who or what may threaten it. Asset identification should cover prompts, retrieved documents, credentials, model outputs, audit logs, tools, external systems, and sensitive business or personal data. Sensitivity classifications establish the potential confidentiality, integrity, safety, and availability impact of compromise. Threat actors and attack vectors must then be enumerated so likelihood and impact can be assessed against specific assets. Mitigation recommendations, residual-risk acceptance, ownership, and final documentation follow the analysis; they cannot be completed coherently before the threats are understood. Anthropic's security transparency materials similarly emphasize maintaining threat models that reflect relevant attacker tactics and techniques. Anthropic Transparency Hub
NEW QUESTION # 18
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