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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Context Management & Reliability | 15% | - Context handling
|
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NEW QUESTION # 64
During testing, when a customer says, "I need a refund for my recent purchase," the agent immediately invokes process_refund but populates the required order_id parameter with a plausible-looking fabricated value instead of first calling lookup_order. The refund fails because the invented order identifier does not exist. Which change directly addresses the root cause of the fabricated order_id?
Answer: B
Explanation:
Option A corrects the missing tool contract that permits Claude to guess a required parameter.
The process_refund description should state its prerequisite explicitly: invoke lookup_order, use only the exact order_id returned by that successful call, and ask for additional identifying information when lookup cannot resolve an order. An input example can demonstrate the correct two-tool sequence.
Anthropic's tool-definition guidance identifies detailed descriptions as the most important factor in tool performance. Descriptions should explain when a tool should and should not be used, what every parameter means, and all important limitations. The tool-use overview confirms that Claude selects and populates tools based on the request and their descriptions.
NEW QUESTION # 65
The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?
Answer: C
Explanation:
In a coordinated multi-agent pipeline, the coordinator mediates information flow. It collects outputs from the web search agent and includes the relevant findings in the prompt when invoking the document analysis agent, ensuring proper sequencing and context without tightly coupling the subagents.
NEW QUESTION # 66
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: C
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 # 67
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.
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: D
Explanation:
The error response must explicitly communicate both control semantics and user-facing meaning. Setting
"retriable": false tells the agent that repeating the same operation cannot change the outcome. Providing a customer-friendly explanation allows Claude to respond accurately without exposing internal implementation details or inventing its own interpretation of the business rule.
Anthropic recommends returning failed tool operations with an error indicator and sufficiently informative content so Claude can decide whether to retry, request another input, or explain the limitation. Tool interfaces should provide detailed descriptions and structured parameters rather than forcing Claude to infer operational behavior from ambiguous text. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/build-a-tool- using-agent?utm_source=chatgpt.com ) Option A appropriately limits automatic retries to technical failures, but it does not solve the stated customer- response problem unless business failures also carry clear semantics. Option B relies on fragile parsing of human-readable messages. Option C adds latency and another tool dependency, and eligibility may still change or fail for reasons not covered by the preliminary check.
A complete schema should identify the error code, category, retryability, customer-safe explanation, and permitted next actions. Technical errors can similarly return "retriable": true with retry guidance and a maximum-attempt policy.
Official references/topics: Structured tool errors, retryability classification, customer-safe explanations, resilient MCP contracts.
NEW QUESTION # 68
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.
Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got `2 to 3'". Retrying these requests without modification produces identical failures.
What's the most effective approach to recover from these validation failures?
Answer: D
Explanation:
An unchanged retry repeats the same task specification and therefore commonly reproduces the same invalid interpretation. The validator has generated precise corrective information--quantity requires a float, but the model returned the range string 2 to 3. Supplying that error in a follow-up turn converts a generic retry into an iterative repair operation.
Anthropic identifies iterative refinement as a method for detecting and correcting inconsistencies by feeding an earlier output back into a subsequent request with targeted instructions. Option A applies that pattern directly. Claude receives the invalid output, the exact Pydantic error, and an instruction to return a schema-compliant correction. The application should cap retries, retain the original source, and escalate cases that cannot be represented without information loss.
NEW QUESTION # 69
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