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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications - Tool safety, reliability, and usability |
| Claude Code Configuration & Workflows | 20% | - Claude Code usage and configuration - Developer productivity workflows - Integrating Claude Code into development processes |
| Context Management & Reliability | 15% | - Managing context windows and information flow - Evaluation and reliability strategies - Production deployment considerations |
| Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Structured output generation and validation - Prompt design strategies |
| Agentic Architecture & Orchestration | 27% | - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns - Designing agentic systems and workflows |
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NEW QUESTION # 110
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.
Your team has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response ) in addition to tools.
How do these MCP prompts become accessible within Claude Code?
Answer: B
Explanation:
MCP prompts are exposed as user-invoked commands rather than autonomous tools or permanently loaded system instructions. Claude Code dynamically discovers prompts from connected MCP servers and displays them in the command list using the naming convention /mcp__servername__promptname .
Arguments are supplied as space-separated values after the command. When executed, the MCP server resolves the prompt and its returned content is injected into the active conversation. Anthropic's official documentation provides examples such as /mcp__github__list_prs and /mcp__jira__create_issue "Bug in login flow" high . ( https://code.claude.com/docs/en/mcp ) Option A would consume context continuously and incorrectly treat optional workflow templates as mandatory system instructions. Option B confuses MCP prompts with MCP tools: tools are model-callable operations, while prompts are reusable prompt templates invoked as commands. Option C describes MCP resources, which can be referenced and attached but are a distinct MCP capability.
For the stated server, the team could invoke commands such as /mcp__devops__deploy_checklist or
/mcp__devops__incident_response service-name . The exact server segment is derived from the configured server name, with normalization applied where necessary.
Official references/topics: MCP Prompts; Dynamic Prompt Discovery; MCP Slash-Command Naming; Prompt Arguments.
NEW QUESTION # 111
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: B
Explanation:
Option B follows the Claude Agent SDK's default context-isolation model. A newly spawned subagent receives a fresh context window and does not automatically inherit the parent's conversation history or previous tool results. The coordinator must therefore include the information required for synthesis directly in the spawning prompt, preferably using clearly separated structured sections for web findings, document findings, source metadata, conflicts, and unresolved questions.
Anthropic's official SDK subagent documentation states that the Agent tool's prompt string is the content passed from the parent to the new subagent. It specifically advises including required file paths, errors, and decisions in that prompt because parent context is not inherited.
Option A could work only if the application had deliberately implemented and authorized such a shared- memory architecture. The question establishes no such mechanism, and reference identifiers alone provide no evidence to the subagent. Option C introduces a callback protocol that is unnecessary for a normal handoff.
Option D is incorrect because subagent isolation expressly prevents automatic inheritance. "Complete findings" means the complete evidence needed for synthesis, not every intermediate search trace or irrelevant tool result.
NEW QUESTION # 112
A healthcare company processes long clinical reports. Some exceed Claude's practical context requirements. What is the BEST architectural approach?
Answer: C
Explanation:
Chunking divides large documents into manageable sections while preserving important information. Individual chunks can be summarized or indexed before aggregation, allowing Claude to process lengthy documents efficiently without losing essential context.
NEW QUESTION # 113
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 asks your agent to add comprehensive tests to a legacy codebase with 200 files and minimal existing test coverage. The engineer hasn't specified which modules to prioritize.
How should the agent decompose this open-ended task?
Answer: B
Explanation:
The task is open-ended because neither the critical modules nor the required testing sequence is known in advance. The agent should first use lightweight discovery tools to map the repository, locate existing tests, identify central modules, and determine which components have high fan-in, business significance, complex branching, or extensive external dependencies. It can then produce an initial risk-based testing plan and refine it as new dependency information appears.
Anthropic distinguishes predefined workflows from agents that dynamically control their processes and tool usage. Agents are appropriate when the required steps cannot be reliably hardcoded and must adapt to environmental evidence. During execution, they should obtain ground truth through tool results and use that feedback to determine subsequent actions.
Anthropic also identifies orchestrator-worker designs as suitable for complex coding and search tasks where the necessary subtasks depend on what the investigation reveals.
NEW QUESTION # 114
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: D
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.
NEW QUESTION # 115
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