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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Task decomposition and dynamic subagent selection - Error recovery, guardrails and safety patterns |
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Token budget management and cost control - Context pruning and summarization strategies - Idempotency, consistency and failure resilience |
| Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Error handling and tool response formatting - Tool distribution and permission controls - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 |
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - System prompt design and persona alignment - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting |
| Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions |
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NEW QUESTION # 117
An enterprise wants Claude responses to remain professional across every interaction. Where should tone instructions primarily reside?
Answer: C
Explanation:
Persistent behavioral guidance belongs in the system prompt. Defining tone centrally ensures consistent communication across conversations regardless of varying user inputs or retrieved knowledge.
NEW QUESTION # 118
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?
Answer: D
Explanation:
Forking preserves the accumulated authentication-module context while giving each refactoring approach an independent session, preventing one exploration from influencing the other.
Anthropic's Agent SDK supports creating a new session ID when resuming with session forking enabled.
NEW QUESTION # 119
You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
Your multi-agent research pipeline crashes after processing 12 of 28 documents. The web-search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun identifying patterns. You need to resume processing without repeating work or losing fidelity in the prior findings.
What state-management approach best balances information fidelity with context efficiency when restoring agent state?
Answer: B
Explanation:
Option B combines lossless persistence with selective restoration. Each agent should checkpoint structured records containing completed document IDs, extracted findings, source metadata, unresolved work, validation status, and schema version. A coordinator-level manifest records where those artifacts are stored and which pipeline stages completed. On resumption, the coordinator can skip the 12 completed documents and inject only the artifacts relevant to each restarted agent. Anthropic's effective context-engineering guidance recommends structured note-taking outside the context window so agents can preserve project state across resets while loading only the information needed later. A vector store, option A, is useful for relevance retrieval but can omit exact details through similarity-based selection and is unsuitable as the sole recovery record. Option C fragments ownership of recovery state and makes cross-agent consistency difficult to verify.
Option D preserves an extensive trace, but replaying a complete conversation wastes context and mixes obsolete instructions, intermediate reasoning, and redundant outputs. Structured checkpoints plus a manifest provide deterministic recovery, auditability, exact provenance, and efficient prompt reconstruction.
Checkpoints should be written atomically and validated before the corresponding stage is marked complete.
NEW QUESTION # 120
Your process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (5% of calls), and business errors ("Order exceeds
30 day return window", "Item already refunded") that are permanent (12% of calls). Monitoring shows the agent wastes 3-4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude. What's the most effective way to reduce wasted retries while improving customer-facing response quality?
Answer: A
Explanation:
Structured error responses let the agent distinguish permanent business-rule failures from transient technical failures without parsing plain text. Marking business errors as non-retriable and including a customer-friendly explanation prevents wasted retry turns while helping the agent give a clear, useful response to the customer.
NEW QUESTION # 121
You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?
Answer: D
Explanation:
Claude Code supports resuming a specific saved session by either its identifier or its assigned name. Because the engineer knows the target session is named auth-deep-dive, the appropriate command is:
claude --resume auth-deep-dive
Anthropic's CLI reference explicitly states that --resume can resume a session by ID or name and gives named-session usage in the same form. Resuming restores the relevant conversation history and accumulated context, including the prior analysis and files discussed during the investigation.
NEW QUESTION # 122
......
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