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| Section | Weight | Objectives |
|---|---|---|
| Claude Models, Prompting & Context Engineering | 13% | - Context window optimization - System prompts and prompt templates - Claude model selection and trade-offs - Guardrails - Prompt reuse and context engineering strategies |
| Solution Design & Architecture | 17% | - End-to-end architecture design - Translating business problems into Claude-based AI solutions - Multi-agent systems and orchestration - Architectural patterns
- Decomposition techniques for complex problem solving |
| Integration | 19% | - Authentication and authorization analysis - RAG pipeline design
- Claude integration mechanisms
|
| Evaluation, Testing & Optimization | 16% | - Evaluation metrics and datasets - Production monitoring and optimization - Cost and performance optimization - A/B testing - System issue diagnosis - Evaluation framework design |
| Developer Productivity & Operational Enablement | 7% | - Claude tooling configuration for teams - AI-assisted developer workflows - Developer enablement - Debugging and operational issue resolution |
| Governance, Safety & Risk Management | 14% | - Ethical AI considerations - Human-in-the-loop validation - AI safety and guardrails - Security and risk management - Regulatory and compliance requirements |
| Stakeholder Communication & Lifecycle Management | 14% | - Solution lifecycle management - Service-level agreements - Discovery and requirements gathering - Stakeholder management - Architecture documentation - Communicating architectural decisions |
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NEW QUESTION # 25
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: B,E
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 # 26
You are defining when to introduce a project subagent versus relying on Claude Code's general capabilities.
Which scenario most directly justifies a dedicated subagent?
Answer: A
Explanation:
A dedicated subagent is justified when specialization is recurring and operationally valuable. Option A allows the team to encode a database-review role once, including its system prompt, triggering description, permitted tools, model, and review criteria. This improves consistency across sessions, isolates specialized context, and applies least privilege by withholding unrelated capabilities. Anthropic's custom subagent documentation supports task-specific prompts, model selection, tool allowlists, permission modes, hooks, skills, and persistent memory. A one-time question does not justify the maintenance overhead of a reusable agent definition, while Options C and D explicitly provide no specialization requirement. The subagent should also have clear invocation conditions, evaluation cases, ownership, and lifecycle review.
Study Guide references/topics: Claude Code subagents; task specialization; context isolation; model routing; scoped permissions; reusable automation.
NEW QUESTION # 27
You are producing an architecture guide for a new deployment and must complete the planning steps before drafting each section.
Which two steps must be completed BEFORE drafting each section of the guide? (Select two.) Each correct answer presents part of the solution.
Answer: A,E
Explanation:
Effective documentation begins with audience analysis and structural planning. Identifying the audience determines the required technical depth, terminology, decision context, and operational emphasis. Defining the questions the guide must answer prevents sections from becoming generic descriptions. The outline then establishes complete coverage across architecture overview, component responsibilities, interface contracts, dominant flows, operational runbooks, and known limitations. Translation occurs only after stable source content exists. Implementation-team validation also follows an initial draft because there must be material to review. Version control, reviewers, and cadence are important governance controls, but they do not replace the two content-planning activities required before section drafting. The guide should be planned around the decisions and tasks its intended readers must perform.
NEW QUESTION # 28
You are evaluating an evaluation set used to score a Claude-based hiring-support tool. The set is drawn from one geographic region and one tenure band.
Which response is most appropriate?
Answer: D
Explanation:
The existing evaluation set does not represent the population or operating conditions the hiring-support system will encounter. Option B corrects this coverage defect by expanding geographic and tenure slices and requiring the system to be rescored before broader deployment.
Aggregate performance on one narrow group can conceal substantial differences among regions, experience levels, job families, languages, and other relevant cohorts. The expanded dataset should therefore support disaggregated metrics, not merely one combined score. It should also include representative, edge, and adversarial cases, with labels and grading procedures reviewed for consistency and potential bias.
Anthropic's evaluation guidance states that evaluations should mirror the real-world task distribution and include edge cases. Success criteria should also be relevant to the application's actual purpose and users.
Define Success Criteria and Build Evaluations
Option A replaces one unrepresentative method with another. Option C deliberately narrows coverage further.
Option D mistakes high performance on a restricted subset for evidence of generalization.
Because hiring is a consequential domain, the organization should combine representative quantitative evaluation, subgroup analysis, expert review, monitoring, access controls, transparency, and meaningful human responsibility for final decisions.
Study Guide references/topics: Dataset representativeness; evaluation slices; subgroup performance; bias detection; consequential-use governance; pre-release rescoring.
NEW QUESTION # 29
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 # 30
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