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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Model Context Protocol (MCP) concepts and integration - Designing effective tools for Claude applications |
| Claude Code Configuration & Workflows | 20% | - Integrating Claude Code into development processes - Developer productivity workflows - Claude Code usage and configuration |
| Context Management & Reliability | 15% | - Managing context windows and information flow - Production deployment considerations - Evaluation and reliability strategies |
| Agentic Architecture & Orchestration | 27% | - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures - Designing agentic systems and workflows |
| Prompt Engineering & Structured Output | 20% | - Improving Claude response quality and consistency - Structured output generation and validation - Prompt design strategies |
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NEW QUESTION # 47
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.
Your pipeline uses a tool called extract_metadata with a JSON schema for paper details. You've also defined lookup_citations and verify_doi tools for enrichment. During testing, you notice that when users include requests like "extract the metadata and tell me how cited it is," Claude sometimes calls lookup_citations first, which fails because it needs the DOI that extract_metadata would provide.
What's the most effective way to ensure structured metadata extraction happens first?
Answer: D
Explanation:
The dependency must be enforced by orchestration rather than left to probabilistic tool selection. Anthropic documents that tool_choice: {"type": "tool", "name": "..."} forces Claude to invoke the specified tool. By contrast, auto allows Claude to decide whether and which tool to call, while any requires some tool but does not force a particular one. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/define-tools ) Option A therefore establishes a deterministic two-stage workflow. The first API turn forces extract_metadata
, producing the DOI and other structured paper details. The application validates and stores that result. A subsequent turn then exposes or permits verify_doi and lookup_citations , passing the extracted DOI as explicit state. This design converts an implicit tool dependency into an application-controlled execution graph.
Option B is incorrect because array order is not a documented precedence mechanism and cannot guarantee selection. Option C forces extract_metadata on every call, including turns where enrichment should occur, potentially creating an infinite or non-progressing workflow. Option D guarantees only that one available tool is called; Claude could still select lookup_citations before the DOI exists.
For stronger input integrity, the tools can also use strict schemas so their arguments conform to the declared JSON Schema. The sequencing requirement, however, remains the responsibility of the orchestration layer.
Official references/topics: Tool Choice; Forced Tool Invocation; Multi-Turn Tool Orchestration; Tool Dependency Management.
NEW QUESTION # 48
You have configured the system so that all four subagents have access to the complete set of 18 tools. During testing, agents frequently call tools outside their specialization-the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool-selection behavior?
Answer: A
Explanation:
Option A identifies the direct configuration contradiction. Each system prompt defines a specialized responsibility, but the corresponding tool allowlist presents unrelated actions as available capabilities. The synthesis agent may therefore interpret web searching as an acceptable way to fill evidence gaps, while the report generator may perform document analysis instead of limiting itself to report production.
Anthropic's subagent documentation defines subagents as focused workers with their own prompts and specific tool access. Its tool-definition guidance also recommends reducing selection ambiguity by presenting clear, relevant tools. The appropriate correction is least-capability configuration: expose search tools to the search agent, document tools to the analyzer, synthesis utilities to the synthesizer, and formatting or output tools to the report generator.
Option B may affect token efficiency but does not explain the role-specific misuse pattern. Option C concerns delegation, whereas the improper calls occur after successful delegation. Option D is tempting, but Anthropic does not define four or five tools as a universal reliability threshold; 18 tools alone does not prove threshold failure. The decisive defect is misalignment between declared roles and permitted capabilities.
NEW QUESTION # 49
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 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 mid-process escalation should transfer the decision-ready state accumulated by the agent. The human reviewer needs the verified customer identity, relevant order information, the promotional-pricing discrepancy, the reason approval is required, and any actions already attempted. Option B preserves this information in a concise, structured handoff while avoiding unnecessary repetition of the complete raw transcript.
Anthropic's tool-design guidance recommends returning high-signal information and stable identifiers containing only what Claude or the next workflow participant needs to determine the next action. Anthropic's context-engineering guidance similarly advocates structured notes that preserve critical state and dependencies without retaining every redundant tool result. A structured escalation payload applies both principles and reduces handling time for the manager. ( https://platform.claude.com/docs/en/agents-and-tools
/tool-use/define-tools )
Option A discards the investigation already completed. Option C violates the agent's authorization boundary and risks an impermissible financial action. Option D provides auditability, but a reference ID alone forces the human to reconstruct the case from an excessively broad transcript. Human control must remain meaningful when an agent encounters a decision outside its authority; the agent should pause and hand the decision back with sufficient supporting context. ( https://www.anthropic.com/research/trustworthy-agents ) Official references/topics: Structured agent handoffs, high-signal tool results, human-control boundaries, persistent structured state.
NEW QUESTION # 50
Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document-analysis agent frequently identifies evidence gaps--for example, noting that
"the retrieved sources discuss API authentication but lack details about token-refresh patterns." Under the current strict pipeline, this insight is not actionable because searching has already finished. What is the most effective architectural change?
Answer: B
Explanation:
Option C converts the linear pipeline into a controlled iterative research loop. The document- analysis agent is best positioned to identify precisely what the retrieved evidence fails to answer.
It should return structured gaps containing the missing question, evidence already examined, preferred source characteristics, and the coverage criterion that remains unsatisfied. The coordinator can then issue focused searches and resubmit the new material for analysis.
Anthropic's multi-agent research architecture uses a lead agent to decompose work, coordinate research agents, assess returned information, and continue investigation when further evidence is required. The guidance also stresses clear objectives, output formats, source requirements, and task boundaries to prevent gaps and duplicated work.
NEW QUESTION # 51
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?
Answer: D
Explanation:
Option C is the strongest choice among the listed approaches because it moves structured data generation from unconstrained response text into a schema-governed interface. Prompt-only JSON instructions, regular-expression extraction, and corrective retries are probabilistic recovery mechanisms: they may reduce failures, but none guarantees that the first response satisfies the contract. Anthropic now provides two schema-constrained mechanisms: JSON Structured Outputs through output_config.format and strict tool use through a tool definition with strict: true.
Strict tool use uses grammar-constrained sampling so the tool input conforms to the declared JSON Schema, including required properties and data types. Therefore, the production implementation represented by option C should define the extraction tool's input_schema and enable strict mode. The application reads the tool_use block as the extracted record; it does not need to scrape prose or search for braces.
NEW QUESTION # 52
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