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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Tool distribution and permission controls - Error handling and tool response formatting - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 |
| Topic 2: Prompt Engineering & Structured Output | 20% | - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - System prompt design and persona alignment |
| Topic 3: Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution |
| Topic 4: Context Management & Reliability | 15% | - Context pruning and summarization strategies - Idempotency, consistency and failure resilience - Context window optimization and prioritization - Token budget management and cost control |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Session state management and workflow enforcement |
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NEW QUESTION # 123
When implementing your lookup_order MCP tool, the backend sometimes returns errors-for example,
"Order not found" or temporary database failures. What is the correct pattern for communicating these errors back to the agent?
Answer: A
Explanation:
Option A follows the MCP error contract. An error originating during execution of a valid tool call-such as an API failure, input-validation problem, or business-logic failure-should be returned inside the tool result with isError: true. The content should explain what happened and, where possible, provide actionable recovery guidance.
The official MCP tools specification distinguishes tool-execution errors from protocol-level errors and specifies that execution failures are reported through tool results using isError: true. Anthropic's tool-using agent tutorial provides the equivalent Claude API pattern using is_error: true, allowing Claude to retry with corrected input, request clarification, or explain the limitation.
Option B falsely represents a failed operation as successful, weakening reliable failure detection. Option C deprives the agent of the information required to determine an appropriate recovery action. Option D may be converted into a tool error by some SDK runners, but uncaught exceptions are not the MCP-level response contract. The strongest implementation returns a concise, sanitized, actionable error result and separately records detailed diagnostics in server logs.
NEW QUESTION # 124
Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval - beyond the agent's authorization level. How should the workflow handle this mid-process escalation?
Answer: A
Explanation:
A structured handoff containing the customer details, order information, and the specific issue ensures the human agent has all relevant context to act immediately. This approach avoids delays or repeated clarification and preserves continuity when authority limits prevent the agent from completing the task.
NEW QUESTION # 125
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: C
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 # 126
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.
After the web-search agent finds 25 sources containing 120,000 tokens of raw content, the document-analysis agent extracts 15,000 tokens of key insights, and the synthesis agent produces a coherent 3,000-token narrative draft, the coordinator must pass context to the report-generation agent for the final output with proper source citations.
What context-passing strategy provides the best balance of completeness and efficiency?
Answer: B
Explanation:
Option A gives the report generator the finished narrative context plus the evidence required to verify and cite its claims. The 3,000-token synthesis draft supplies organization and conclusions, while the source index preserves URLs, supporting excerpts, and claim-to-source mappings without transferring 120,000 tokens of raw search material. Anthropic's effective context-engineering guidance recommends curating the smallest high-signal context and having subagents return condensed, distilled results rather than flooding later stages with their complete working history. Anthropic's citation guidance likewise depends on retaining specific supporting passages and source locations. Option B is complete but inefficient and introduces context pollution that can reduce attention to relevant evidence. Option C separates citation insertion from the reasoning that produced the claims, making incorrect or unsupported matches more likely. Option D retains source names but discards the passages needed to demonstrate that each source actually supports the associated claim. The structured index should use stable source IDs and include only the excerpts necessary for the report's factual claims, with validation that every cited claim maps to at least one evidence entry.
NEW QUESTION # 127
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.
Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: { " isError " : true, " content " : [{ " type " : " text " , " text " : " Operation failed " }]} . The agent cannot distinguish between error types.
What's the most effective improvement?
Answer: C
Explanation:
The agent is behaving inconsistently because every failure is represented identically. "Operation failed" contains no information about permanence, user correctability, authorization, or whether another attempt is likely to succeed. Adding structured fields converts the error into an actionable interface contract.
Anthropic instructs tool implementations to mark failures using an error flag and return information Claude can use to retry with corrected input, ask for clarification, or explain a limitation. Tool design should expose sufficient detail for the model to choose the correct next action rather than forcing it to infer the cause from a generic message. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/build-a-tool-using-agent?
utm_source=chatgpt.com )
With error_category , isRetryable , and a causal description, the agent can retry transient network failures, request corrected identifiers for validation errors, and escalate or report permission failures without pointless repetitions. Option B retries permanent validation and permission failures unnecessarily. Option C adds another tool call and failure point merely to classify information the original tool already possesses. Option D relies on parsing variable text and examples rather than providing explicit machine-readable semantics.
The tool should preserve isError: true , return a stable structured error object, include a safe customer-facing message where appropriate, and avoid exposing internal secrets or stack traces.
Official references/topics: MCP error contracts, actionable tool results, retryability metadata, agent- computer interface design.
NEW QUESTION # 128
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