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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting - MCP tool, resource and prompt implementation |
| Topic 2: Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement |
| Topic 3: Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules |
| Topic 4: Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection |
| Topic 5: 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 # 128
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.
Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output.
How should you modify the system to run these subagents concurrently?
Answer: C
Explanation:
Option B expresses both independent delegations in the same orchestration turn, allowing the runtime to execute them concurrently and return their results together. The current Claude Agent SDK calls the subagent- spawning capability the Agent tool; Task is its former name. Anthropic's SDK subagent documentation explicitly identifies parallel analysis as a primary subagent use case. Claude tool responses can also contain multiple tool_use blocks , enabling independent calls to be handled as one parallel group rather than as serial model turns. Option A shortens each execution but does not remove the unnecessary wait between them and may reduce analysis quality. Option C provides useful behavioral guidance, but instructions alone do not establish the required response structure; the coordinator must actually emit both calls together. Option D duplicates coordinator execution, complicates state management, and creates unnecessary aggregation work.
The correct flow is parallel fan-out from one coordinator, followed by a synchronization point that validates both results before synthesis begins. Failures should be tracked independently so that only the unsuccessful branch requires retrying.
NEW QUESTION # 129
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent
- this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate). How should you achieve guaranteed compliance?
Answer: C
Explanation:
Enforcing the policy outside of the model via a PreToolUse hook guarantees compliance. By intercepting high-value refund attempts and triggering human escalation, the system ensures the rule is applied consistently regardless of prompt instructions or agent behavior.
NEW QUESTION # 130
After integrating a local MCP server providing code analysis tools (analyze_dependencies, find_dead_code, calculate _complexity), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
MCP: analyze_ dependencies - "Analyzes dependency graph"
Built-in: Grep - "Search file contents for a pattern using regular
expressions. Returns matching lines with line numbers and surrounding
context."
What's the most effective approach to improve the agent's selection of MCP tools?
Answer: C
Explanation:
Claude selects tools primarily from their names and descriptions. A specific description distinguishing dependency-graph analysis-including direct imports, transitive dependencies, and cycles-from simple text search makes analyze_dependencies the clearer choice.
NEW QUESTION # 131
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
Your productivity agent connects to three MCP servers: an issue tracker with search_issues , get_issue , and create_comment ; a documentation wiki with search_docs , get_page , and list_spaces ; and a database explorer with run_query , get_schema , and list_databases . For cross-system questions such as, "Which database tables are affected by the authentication refactor in PROJ-1234?", the agent makes eight to ten sequential exploratory calls, lacks visibility into each server's available content, and exhausts context before completing complex investigations.
What architectural change best leverages MCP capabilities to address these problems?
Answer: B
Explanation:
Option C uses MCP resources for their intended purpose: exposing contextual data that applications and agents can discover and read without treating every lookup as an action-oriented tool invocation. Anthropic's Claude Code MCP documentation states that Claude Code automatically provides mechanisms to list and read resources exposed by connected MCP servers. Resources may contain text, JSON, structured data, or other server-provided content. The official MCP server concepts likewise explain that resources expose information from files, APIs, and databases through identifiable, discoverable URIs. Publishing issue summaries, the wiki hierarchy, and database schemas gives the agent an initial map of available evidence. It can identify relevant systems and retrieve only the necessary records before making targeted tool calls. Option A creates another opaque, high-level tool whose output quality depends on each server interpreting the investigation correctly.
Option B produces tight coupling and increases operational complexity. Option D prevents the cross-system investigation required by the question. Resource catalogs improve discoverability, reduce blind exploration, and preserve tools for searches, queries, and mutations that genuinely require execution.
NEW QUESTION # 132
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.
The coordinator provides detailed step-by-step instructions to the web-search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when the pre-specified searches fail, (2) research quality drops for emerging topics that do not match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What is the most effective way to improve subagent adaptability?
Answer: D
Explanation:
Option A defines what successful research must achieve without hard-coding a brittle search procedure. The subagent receives measurable objectives-breadth, diversity, recency, and relevance-but retains authority to reformulate queries, follow promising leads, and adjust source selection when the topic does not match familiar patterns. Anthropic's account of its multi-agent research system states that subagents need an objective, expected output format, guidance about tools and sources, and clear task boundaries. This is more informative than option B's vague instruction, yet more adaptive than prescribing every query. Option C improves only one known failure path and still constrains the agent to a predefined procedure that may be unsuitable for emerging subjects. Option D adds orchestration complexity and a potentially incorrect classification step without solving over-prescription within either category. The coordinator should describe mandatory constraints separately from strategic guidance: authoritative-source requirements and time boundaries can remain fixed, while query formulation and exploration paths remain agent-controlled.
Evaluation should then measure source coverage, evidence quality, duplication, and discovery of relevant secondary leads rather than whether the subagent followed a particular series of searches.
NEW QUESTION # 133
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