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| Section | Weight | Objectives |
|---|---|---|
| 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% | - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules |
| Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement |
| Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Error handling and tool response formatting - Tool distribution and permission controls - Tool schema design and interface boundaries |
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Token budget management and cost control - Context pruning and summarization strategies |
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NEW QUESTION # 149
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.
A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers. What task decomposition approach would be most effective?
Answer: C
Explanation:
The relevant components and failure path are initially unknown, so the investigation should adapt as evidence emerges. This matches the orchestrator-workers pattern, where an agent dynamically decomposes a complex task rather than following a fixed sequence or launching broad parallel work prematurely.
NEW QUESTION # 150
After the web search and document analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?
Answer: C
Explanation:
The synthesis subagent requires the actual content from prior subagents to perform its task.
Including the complete findings in its prompt ensures it has the necessary context to accurately synthesize information without relying on indirect references or external state.
NEW QUESTION # 151
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: C
Explanation:
Forking is specifically designed for exploring alternative directions from a shared body of prior analysis. Each fork starts with a copy of yesterday's conversation history, including the files read, architectural observations, dependency findings, and decisions already recorded. The microservice approach and the in-place refactoring approach can then develop independently under separate session IDs.
Anthropic's Agent SDK documentation states that a fork creates a new session from a copy of the original history while leaving the original session unchanged. Each resulting session can subsequently be resumed independently. The documented implementation combines resume with fork_session=True in Python or forkSession: true in TypeScript. ( https://code.claude.com/docs/en/agent-sdk/sessions ) Option B allows conclusions, assumptions, and proposed edits from the first approach to contaminate the evaluation of the second. Option C preserves context for only one branch and forces the engineer to reconstruct context manually for the other. Option D discards the detailed analysis already captured in the session and depends on potentially incomplete summaries.
Two forks provide equivalent starting conditions, preserve the parent investigation, and support a fair comparison of scope, migration risk, operational complexity, and required code changes. Filesystem edits should still be isolated through worktrees or checkpointing because session forking branches conversation history, not the working directory.
Official references/topics: Agent SDK Sessions; Session Forking; Alternative-Approach Exploration; Context Preservation.
NEW QUESTION # 152
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like
'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order , which catches all exceptions and returns a tool_result with is_error: true and the message
"Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools.
Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?
Answer: A
Explanation:
Option B preserves the formal failure indicator while making the returned content operationally useful.
Anthropic's tool-use guidance states that when a tool fails, the application should return the error through tool_result content and mark it with is_error: true . Claude can then use that information to retry with corrected input, seek clarification, or explain the limitation. ( https://docs.anthropic.com/en/docs/agents-and- tools/tool-use/bash-tool ) The two example messages identify both the cause and the appropriate recovery path. "Order not found" indicates that repeating the same identifier will not help and proposes an alternative lookup method.
"Database timeout" identifies a transient infrastructure condition for which a retry is reasonable. This eliminates blind repetition without removing Claude's ability to adapt.
Option A can be useful for tightly bounded low-level retries, but it does not solve permanent errors or tell the agent what happened after retries fail. Option C incorrectly disguises a failed operation as successful tool content. Option D moves semantic knowledge into hardcoded orchestration logic, making the system more rigid and duplicating information already known by the tool.
The most reliable tool interface combines the error flag, a stable error type, a retryability indicator, a concise causal explanation, and a recommended recovery action.
Official references/topics: is_error handling, instructive tool results, recovery guidance, resilient MCP tool design.
NEW QUESTION # 153
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?
Answer: B
Explanation:
Before rewriting or reorganizing the instructions, first confirm that Claude Code is actually loading the intended CLAUDE.md. The /memory command lets you inspect the active memory files, making it the fastest way to diagnose inconsistent loading across sessions.
NEW QUESTION # 154
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