Last Updated: Sep 02, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Evaluation, Testing & Optimization | 16% | - Optimize performance, prompting, and model selection - Implement iterative improvement pipelines - Define evaluation metrics and success criteria - Test accuracy, reliability, latency, and cost |
| 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 |
| Integration | 19% | - Implement Model Context Protocol (MCP) integrations - Integrate Claude with enterprise systems, APIs, and tools - Integrate with data pipelines and RAG systems - Design authentication, authorization, and observability |
| Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Conduct structured discovery and requirement gathering - Manage stakeholder feedback and expectation alignment - Communicate architectural decisions and trade-offs |
| 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 |
| Governance, Safety & Risk Management | 14% | - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Manage data privacy and security compliance |
| Solution Design & Architecture | 17% | - Select architectural patterns: workflow, agentic, augmented LLM - Align solutions to business value pillars - Design end-to-end architectures and feedback loops - Design multi-agent systems and orchestration strategies - Translate business problems into Claude-based AI solutions |
Question 1
You are sequencing decomposed components in an invoice-processing pipeline.
For each of the decomposed components, select the execution layer it belongs to: "Pre-Processing," "Model Stage," or "Post-Processing."
Question 2
You are assessing data-exfiltration risk in a Claude-based assistant that has tools for both internal-document retrieval and outbound HTTP calls.
Which scenario most directly indicates a data-exfiltration risk?
A. Adversarial content in a retrieved document instructs the model to call the outbound HTTP tool and send sensitive content to an attacker-controlled URL.
B. The internal-document retrieval tool returns a document that the user is authorized to view.
C. The outbound HTTP tool returns a 200 response when the assistant calls an allow-listed domain to fulfil a user-initiated data-lookup request.
D. The user submits a routine question and receives a routine answer.
Question 3
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.
A. Centralized log retention for guardrail violations with quarterly review by the security team.
B. Adversarial-input coverage in the evaluation set with regression tracking on guardrail performance.
C. Per-role tool allow-lists enforced at the orchestration layer before any tool call executes.
D. User feedback channels that route reported guardrail failures into the product backlog for triage.
E. Periodic refresh of the system prompt wording to keep refusal language current and clear.
Question 4
You are integrating Claude Code into the team's pull-request workflow. The team wants AI-assisted review without removing human approval.
Which integration design best fits this requirement?
A. Claude Code disables all existing branch-protection rules to streamline the merge process, removing human approval as a required gate.
B. Claude Code merges every pull request automatically after completing its analysis, bypassing human approval and the existing branch-protection rules.
C. Claude Code silently deletes pull requests it assesses as low quality without posting a comment or notifying the author.
D. Claude Code reviews the pull request and posts a structured analysis as a comment, while a human reviewer retains the approval decision under the existing branch-protection rules.
Question 5
During an architectural review, the security team identifies a risk that adversarial content injected into retrieved documents could manipulate the model's behavior.
Which mitigation most directly addresses this threat?
A. Treat all retrieved content as untrusted input and apply input classifiers with output validation.
B. Require citations for each claim and constrain responses to source-supported content.
C. Score outputs against a stable adversarial evaluation set on each model-version change.
D. Restrict outbound tool calls to an approved destination allow-list.
Solutions:
| Question 1 Answer: Only visible for members | Question 2 Answer: A | Question 3 Answer: B,C | Question 4 Answer: D | Question 5 Answer: A |
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