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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration |
| Topic 2: Context Management & Reliability | 15% | - Evaluation and reliability strategies - Production deployment considerations - Managing context windows and information flow |
| Topic 3: Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Selecting appropriate Claude architectures - Agent coordination and orchestration patterns |
| Topic 4: Claude Code Configuration & Workflows | 20% | - Integrating Claude Code into development processes - Claude Code usage and configuration - Developer productivity workflows |
| Topic 5: Prompt Engineering & Structured Output | 20% | - Prompt design strategies - Structured output generation and validation - Improving Claude response quality and consistency |
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NEW QUESTION # 182
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 document-analysis agent has a single analyze_document tool that takes a document and a free-text instruction parameter. During evaluation, requests such as "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require new requests with clarified instructions.
What is the most effective way to improve reliability?
Answer: A
Explanation:
Option B removes ambiguity at the tool-interface level. Extraction, summarization, and claim verification are different operations with different success criteria and output structures. Separate tools allow each operation to have a precise name, purpose, parameter schema, response contract, and error behavior. Anthropic's guidance on writing effective tools for agents recommends a small set of thoughtful tools targeted at specific, high-impact workflows rather than generic wrappers that force the model to infer operational meaning from loosely structured instructions. Tool descriptions and examples, option A, could improve behavior but leave the overloaded free-text contract intact. An analysis_type enum, option C, identifies the requested mode but still requires one tool to return substantially different result structures, increasing validation and downstream branching. Coordinator pre-classification, option D, adds another probabilistic decision without correcting the interface used by the analysis agent. Purpose-specific tools let the synthesis layer know exactly what output it will receive. Their schemas should require relevant fields-for example, data-point names and source locations for extraction, or claim, verdict, and evidence for verification-and should be tested against the observed failure cases.
NEW QUESTION # 183
Your MCP server implements a check_availability tool that queries an external calendar API.
During testing, you encounter three error conditions: (1) the tool is called with a malformed request missing the required user_email parameter, (2) the calendar API returns a 404 because the specified user doesn't exist in the calendar system, and (3) the calendar API returns a 503 because the service is temporarily unavailable. How should each error be reported according to MCP's error handling design?
Answer: A
Explanation:
Errors caused by malformed requests (missing required parameters) violate the RPC protocol and should be reported as JSON-RPC errors. Errors returned by the external service (user not found, temporary unavailability) are valid tool execution results and should be reported as tool outputs with isError set to true, allowing the agent to handle them gracefully.
NEW QUESTION # 184
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
The system needs to extract candidate information (name, contact details, skills, work experience, education) from uploaded resumes. The extracted data must strictly conform to a predefined JSON schema, as missing required fields or incorrect data types will cause downstream validation failures.
What is the most reliable approach to ensure Claude's output consistently matches the schema?
Answer: B
Explanation:
A schema-defined tool provides a structured interface between Claude and the application. The tool's input_schema can declare required properties, nested work-experience and education objects, arrays of skills, and exact data types. Claude then supplies the extracted resume information as tool-call arguments, which the application can read directly from the tool_use response.
Anthropic's tool documentation specifies that a custom tool definition includes a JSON Schema object describing its expected parameters. Current strict tool use can additionally enforce that generated tool arguments match the declared schema exactly. This removes the need to locate JSON inside free-form text or depend on post-generation repair. ( https://docs.anthropic.com/en/docs/agents-and-tools/tool-use/implement- tool-use ) Option A is fragile because regular expressions do not provide a robust parser for arbitrary nested JSON and cannot correct missing properties or incorrect types. Option B improves formatting behavior but remains probabilistic. Option C doubles latency and creates two opportunities for information loss: first during extraction and again during reformatting.
In a production design, fields that may genuinely be absent from a resume should be optional or nullable rather than forcing invented values. The tool schema should also use strict: true where supported. Among the available choices, D provides the strongest structural guarantee and the cleanest downstream integration.
Official references/topics: Tool Use Responses, Input Schemas, Strict Tool Use, Nested Structured Extraction.
NEW QUESTION # 185
Your MCP server includes archive_file(file_id) and delete_file(file_id) tools. Production logs show the agent calls delete_file when users ask to "remove old backups," but company policy requires archiving backup files. Both tools currently have minimal descriptions: "Archives a file" and
"Deletes a file." Which change most directly improves tool selection for this scenario?
Answer: B
Explanation:
Clear tool descriptions directly guide the agent's choice between similar tools. Adding use-case guidance that backup files should not be deleted and should be archived helps the agent map
"remove old backups" to the correct archive operation before execution.
NEW QUESTION # 186
After investigating a billing dispute for more than 25 turns, you determine that duplicate charges resulted from a payment-gateway timeout triggering retry logic. The required refund of $847 exceeds your $500 authorization limit, so you must invoke escalate_to_human. The human agent will not have access to the conversation transcript. What context should you pass to enable effective resolution?
Answer: A
Explanation:
Option C gives the human agent the operational state required to continue without replaying a long conversation. The handoff should include verified identifiers, the duplicate transaction evidence, the diagnosed timeout-and-retry mechanism, the $847 refund requirement, the agent's $500 authorization constraint, completed verification steps, prior actions, and the recommended resolution. Any unresolved uncertainty should be labeled explicitly.
Anthropic's effective context-engineering guidance recommends preserving high-value state in structured notes while removing redundant conversational and tool-call history. Its long-running-agent guidance likewise describes structured handoffs as the mechanism for maintaining continuity across context resets or agent boundaries.
Option A maximizes raw information but forces the human to locate the relevant facts among more than 25 turns, increasing delay and error risk. Option B preserves evidence but omits the authorization constraint, completed verification, and explicit recommended action. Option D is too sparse to support validation or execution. A structured handoff balances fidelity and efficiency: it contains everything needed for the next actor to make the refund decision while excluding greetings, repeated explanations, and irrelevant intermediate tool output.
NEW QUESTION # 187
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