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| Section | Weight | Objectives |
|---|---|---|
| Governance, Safety & Risk Management | 14% | - Ensure regulatory compliance (GDPR, HIPAA, etc.) - Address ethical AI considerations and bias mitigation - Implement guardrails and safety controls - Manage data privacy and security compliance |
| 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 |
| Solution Design & Architecture | 17% | - Translate business problems into Claude-based AI solutions - Design end-to-end architectures and feedback loops - Align solutions to business value pillars - Design multi-agent systems and orchestration strategies - Select architectural patterns: workflow, agentic, augmented LLM |
| Stakeholder Communication & Lifecycle Management | 14% | - Document architectures and support full lifecycle phases - Manage stakeholder feedback and expectation alignment - Communicate architectural decisions and trade-offs - Conduct structured discovery and requirement gathering |
| 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 |
| Claude Models, Prompting & Context Engineering | 13% | - Mitigate prompt injection, leaks, and jailbreak risks - Select appropriate Claude models based on trade-offs - Design system prompts, templates, and guardrails - Apply context engineering and context management techniques |
| Evaluation, Testing & Optimization | 16% | - Implement iterative improvement pipelines - Optimize performance, prompting, and model selection - Define evaluation metrics and success criteria - Test accuracy, reliability, latency, and cost |
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NEW QUESTION # 24
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?
Answer: A
Explanation:
Option A augments the existing review process without transferring final accountability to the model. Claude Code can inspect the change in repository context, identify logic defects, security weaknesses, regressions, and maintainability concerns, and publish structured findings for human evaluation. Existing branch protection then preserves required approvals and separation of duties. Automatic merging or disabling protection rules would convert an advisory capability into an uncontrolled authorization mechanism. Silent deletion would eliminate transparency and prevent authors from challenging or correcting the assessment.
Anthropic's Code Review documentation states that findings are posted as comments and do not approve or block the pull request, leaving the established review workflow intact. Claude Code Review
NEW QUESTION # 25
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 # 26
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: C,D
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 # 27
A team manager wants all engineers working on the same repository to share identical MCP server definitions without manual synchronization.
Which configuration approach satisfies this requirement?
Answer: C
Explanation:
Project-scoped configuration is the appropriate mechanism for repository-specific settings that must remain consistent across collaborators. Claude Code recognizes `.claude/settings.json` as the shared project settings file and `.mcp.json` as the project-level MCP server configuration. When these files are committed to source control, every engineer receives the same definitions through the repository's normal clone, pull, review, and versioning workflow.
Anthropic's scope hierarchy distinguishes project configuration from user and local configuration. Files under the user scope apply to one engineer and are not automatically shared. Operating-system environment variables are also workstation-specific and require separate configuration management. Managed settings can be distributed centrally, but they are primarily intended for organization-wide security policies and controls that must be enforced by administrators; they are less appropriate when the configuration belongs specifically to one repository.
Shared configuration should contain server definitions and environment-variable references, but not plaintext credentials. Each engineer can resolve required secrets through an approved local secret mechanism while retaining identical server names, transports, arguments, and non-sensitive settings. Project MCP servers also remain subject to workspace trust and approval controls before execution.
Study Guide references/topics: [Claude Code configuration scopes](https://docs.anthropic.com/en/docs
/claude-code/settings); project-scoped MCP servers; `.mcp.json`; repository-based operational standardization; secure secret separation.
NEW QUESTION # 28
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 # 29
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