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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developer Productivity & Operational Enablement | 7% | - Claude tooling configuration for teams - AI-assisted developer workflows - Debugging and operational issue resolution - Developer enablement |
| Topic 2: Evaluation, Testing & Optimization | 16% | - Cost and performance optimization - A/B testing - Evaluation metrics and datasets - System issue diagnosis - Evaluation framework design - Production monitoring and optimization |
| Topic 3: Claude Models, Prompting & Context Engineering | 13% | - Claude model selection and trade-offs - Guardrails - System prompts and prompt templates - Context window optimization - Prompt reuse and context engineering strategies |
| Topic 4: Stakeholder Communication & Lifecycle Management | 14% | - Service-level agreements - Stakeholder management - Discovery and requirements gathering - Communicating architectural decisions - Architecture documentation - Solution lifecycle management |
| Topic 5: Governance, Safety & Risk Management | 14% | - Human-in-the-loop validation - Ethical AI considerations - AI safety and guardrails - Regulatory and compliance requirements - Security and risk management |
| Topic 6: Solution Design & Architecture | 17% | - Decomposition techniques for complex problem solving - Alignment with business value, cost, performance, and SLAs - Translating business problems into Claude-based AI solutions - End-to-end architecture design - Architectural patterns
|
| Topic 7: Integration | 19% | - Authentication and authorization analysis - Enterprise system integration - RAG pipeline design
|
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NEW QUESTION # 71
You are reviewing a peer's draft system prompt that contains contradictory instructions: one section says never to speculate beyond the supplied source, while another says to confidently fill in any gaps.
Which response is most appropriate?
Answer: D
Explanation:
The prompt contains mutually incompatible behavioral requirements. The architect should resolve the contradiction explicitly so the model receives one coherent grounding policy. If the application requires source-supported answers, the prompt should prohibit unsupported completion, permit an insufficient- evidence response, and require citations or supporting passages. Adding another priority instruction leaves the underlying conflict in place and creates ambiguity about when speculation is permitted. Increasing temperature makes behavior more variable rather than more correct. Retaining both instructions produces inconsistent outputs across requests and model versions. Anthropic recommends clear, direct, specific prompting and notes that conflicting project instructions can cause Claude to select among them unpredictably. Prompting best practices ; project-instruction troubleshooting
NEW QUESTION # 72
You are supporting an engineer whose newly added MCP server does not appear in their Claude Code session.
Which diagnostic step should be tried first?
Answer: B
Explanation:
The first diagnostic action must establish whether Claude Code has loaded the MCP server configuration and whether it can connect to the registered endpoint. The official management command is claude mcp list; within Claude Code, /mcp can also be used to inspect server status. The listing distinguishes registered and connected servers from configurations that require authentication, approval, or connection troubleshooting.
Connect Claude Code to Tools via MCP
If the server was added or its configuration changed after the current session began, reconnecting or restarting the relevant Claude Code session is a proportionate next step. This ensures that configuration state and available tool definitions are refreshed before more intrusive troubleshooting begins. If the server is listed but unhealthy, the engineer can then inspect it with claude mcp get < name > and investigate transport, command path, credentials, workspace approval, or endpoint connectivity.
Options A, B, and D are operationally unsound because they discard working capabilities or environments before confirming the basic registration state. Disabling all MCP servers eliminates useful diagnostic comparisons; changing product families abandons the relevant configuration; reinstalling the operating system is grossly disproportionate and risks destroying evidence needed for diagnosis.
Study Guide references/topics: Claude Code environment configuration; MCP registration; server-status inspection; session refresh; targeted operational troubleshooting.
NEW QUESTION # 73
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."
Answer:
Explanation:
Explanation:
Model Stage, Post-Processing, Post-Processing, Model Stage, Pre-Processing, Pre-Processing Pre-processing prepares source material before Claude receives it. Optical character recognition converts scanned invoice images into machine-readable text. Because PII redaction is explicitly performed before model invocation, it also belongs in pre-processing. Additional activities at this layer can include file validation, malware scanning, normalization, page separation, and metadata extraction.
The model stage contains tasks requiring Claude's language-understanding capability. Classifying the invoice type involves interpreting textual and contextual features, while extracting structured invoice fields requires mapping unstructured content into defined business attributes.
Post-processing verifies and commits the model's result. Schema validation confirms that required fields exist, data types are correct, enumerated values are permitted, and structural constraints are satisfied.
Persistence must occur only after validation and any required human review because the system of record should not receive malformed or unapproved model output.
This decomposition separates probabilistic inference from deterministic processing. OCR, redaction, validation, and persistence do not need to be delegated to Claude when conventional components can execute them more predictably. The resulting architecture reduces model workload, strengthens privacy controls, improves auditability, and prevents unvalidated output from directly changing authoritative records.
Study Guide references/topics: Task decomposition; pre-processing; model inference; post-processing; deterministic validation; privacy-by-design; system-of-record protection.
NEW QUESTION # 74
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: B
Explanation:
An evaluation set must represent the populations, operating conditions, and use cases that the deployed system will encounter. A dataset drawn from only one geographic region and one tenure band cannot reliably demonstrate that the hiring-support tool performs consistently across its intended users. Expanding the evaluation set enables architects to identify regional performance differences, tenure-related errors, unsupported generalizations, and potentially discriminatory outcomes before broader deployment. The system should then be rescored using the expanded dataset, with results examined across relevant population slices rather than relying only on an aggregate score. Options A and B preserve or worsen the representation gap.
Option D is also inappropriate because qualitative feedback can supplement structured evaluation but cannot replace measurable, repeatable testing of accuracy, fairness, and reliability across the intended deployment population.
NEW QUESTION # 75
You are distinguishing functional from non-functional requirements during discovery.
Which item is a non-functional requirement?
Answer: A
Explanation:
Option D defines a measurable performance characteristic rather than a business capability. The requirement specifies a percentile-based latency threshold-under 800 milliseconds at the 95th percentile-and ties that threshold to the expected workload volume. It therefore describes how well the system must operate under defined conditions.
Options A, B, and C are functional requirements. They describe actions the solution must perform: extracting fields and creating a downstream record, producing an editable draft, and assigning tickets to categories.
Functional requirements define observable system behaviors or outputs.
A strong non-functional requirement must include a metric, threshold, measurement method, and operating condition. For Option D, the architect should further define whether latency is measured end-to-end or only at the model API, the test duration, concurrency, input-size distribution, cache state, timeout behavior, geographic region, and treatment of failed requests. Without those details, teams may report incompatible results while claiming to satisfy the same requirement.
Non-functional requirements for Claude solutions commonly address latency, availability, throughput, cost, privacy, security, recoverability, observability, scalability, and regulatory compliance.
Study Guide references/topics: Structured discovery; functional requirements; non-functional requirements; p95 latency; workload conditions; measurable acceptance criteria.
NEW QUESTION # 76
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