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| Section | Weight | Objectives |
|---|---|---|
| Developer Productivity & Operational Enablement | 7% | - Debugging and operational issue resolution - Claude tooling configuration for teams - AI-assisted developer workflows - Developer enablement |
| Claude Models, Prompting & Context Engineering | 13% | - System prompts and prompt templates - Claude model selection and trade-offs - Context window optimization - Guardrails - Prompt reuse and context engineering strategies |
| Integration | 19% | - Enterprise system integration - Claude integration mechanisms
- RAG pipeline design
|
| Solution Design & Architecture | 17% | - Alignment with business value, cost, performance, and SLAs - End-to-end architecture design - Multi-agent systems and orchestration - Architectural patterns
- Translating business problems into Claude-based AI solutions |
| Governance, Safety & Risk Management | 14% | - Security and risk management - Human-in-the-loop validation - AI safety and guardrails - Ethical AI considerations - Regulatory and compliance requirements |
| Stakeholder Communication & Lifecycle Management | 14% | - Stakeholder management - Solution lifecycle management - Service-level agreements - Discovery and requirements gathering - Architecture documentation - Communicating architectural decisions |
| Evaluation, Testing & Optimization | 16% | - System issue diagnosis - A/B testing - Production monitoring and optimization - Evaluation metrics and datasets - Cost and performance optimization - Evaluation framework design |
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NEW QUESTION # 63
You are rolling out a standardized Claude Code configuration to an engineering team and must complete the planning steps before piloting the configuration.
Which two steps must be completed BEFORE piloting the configuration with a small group of engineers?
(Select two.)
Each correct answer presents part of the solution.
Answer: C,E
Explanation:
Before a pilot begins, the architect must understand the workflows being enabled, the associated security boundaries, and the correct ownership of each configuration decision. Option D establishes this foundation.
Organization-wide, non-overridable controls belong in managed configuration, whereas repository-specific MCP servers, shared permissions, and team subagents generally belong in project scope.
Option A then converts those decisions into an initial project baseline that the pilot group can use. The baseline should identify approved MCP endpoints, minimum permission rules, denied operations, subagent responsibilities, configuration ownership, and observable success criteria. Anthropic distinguishes managed, user, project, and local scopes according to who they affect and whether they are shared with the team. Claude Code Configuration Scopes Options B and C are broad-rollout activities and should not occur before pilot evidence is available. Option E is explicitly post-pilot: findings must first be collected before the configuration can be iterated and stabilized.
The correct lifecycle is discovery, boundary definition, baseline design, limited pilot, evidence review, baseline refinement, documentation and training, followed by controlled expansion with a support and rollback process.
Study Guide references/topics: Claude Code rollout planning; scope ownership; MCP baselines; permission design; pilot strategy; operational enablement.
NEW QUESTION # 64
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 # 65
You are classifying chunking strategies by the corpus type each is best suited to.
For each chunking strategy, select the appropriate corpus type: "Long Structured Documents,"
"Heterogeneous Short Records," or "Code or Hierarchical Specifications."
Answer:
Explanation:
Explanation:
Function-level or section-level chunking for code modules - Code or Hierarchical Specifications Tree-aware chunking that follows code or specification hierarchy - Code or Hierarchical Specifications Per-record chunking where each record is one chunk - Heterogeneous Short Records Semantic chunking along clause or paragraph boundaries - Long Structured Documents Fixed-size chunking with overlap for short records of similar length - Heterogeneous Short Records Hierarchical chunking that mirrors document section structure - Long Structured Documents Chunking must preserve the structural unit that carries meaning in the source corpus. Code and hierarchical specifications are best divided at function, module, class, or tree boundaries because arbitrary token cuts can separate definitions from their implementation or parent context. Heterogeneous short-record collections should generally preserve each record as an independent chunk. Fixed-size overlapping chunks are also effective when records have broadly similar lengths and lack meaningful internal hierarchy. Long structured documents benefit from semantic boundaries such as clauses and paragraphs, while hierarchical chunking preserves relationships among sections, subsections, and parent headings. Anthropic notes that chunk size, boundaries, and overlap materially affect retrieval performance; therefore, one universal chunking method is inappropriate. Anthropic Contextual Retrieval
NEW QUESTION # 66
A Claude Architect is reviewing a post-deployment performance report for an AI-assisted legal-document summarization system. The report includes these observations:
* Average summarization time decreased from 47 minutes to 6 minutes per document.
* Associates spend less time on summaries, but overall billable output has not measurably changed.
* Infrastructure costs increased by 22% because redundant retry logic generated additional API calls.
* Some summaries require attorney correction, adding an average of 8 minutes of review per document.
Which analysis correctly attributes each observation to the appropriate business-value pillar?
Answer: C
Explanation:
Observation 1 is a direct efficiency improvement because the time required for one summarization task fell substantially. Observation 2 shows that local time savings have not yet translated into higher organizational productivity or billable output. Observation 3 is explicitly a solution-cost problem caused by unnecessary API calls. Observation 4 introduces additional human correction time and therefore partially offsets the gross efficiency gain reported in Observation 1. Option D is the only answer that keeps these value dimensions distinct. Measuring only generation speed would overstate realized value because review effort, retry costs, and downstream output must also be included. A valid post-deployment assessment should calculate net cycle time, cost per accepted summary, correction rate, and resulting business output.
NEW QUESTION # 67
You are compiling continuity practices that span the deployment lifecycle.
Which two practices belong on the list? (Select two.)
Each correct answer presents a complete solution.
Answer: A,C
NEW QUESTION # 68
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