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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Context Management & Reliability | 15% | - Context handling
|
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NEW QUESTION # 53
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.
After adding an MCP server with specialized code refactoring tools (extract_function, rename_variable, inline_function), You notice the agent still uses basic text manipulation via Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy.
Examining the configuration, you find each MCP tool has a minimal description like
"extract_function: Extracts a function from code."
What's the most effective way to improve adoption of the MCP refactoring tools?
Answer: B
Explanation:
The minimal descriptions do not distinguish the specialized refactoring tools from general- purpose Write or Bash. Anthropic recommends detailed descriptions that specify what a tool does, when to use it, when not to use it, and its inputs and outputs.
NEW QUESTION # 54
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 # 55
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: A
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 # 56
The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?
Answer: D
Explanation:
Defining specialized subagents is not enough; the coordinator must also be allowed to use the Task tool that actually invokes them. If Task is missing from the coordinator's allowed tools, the model may describe delegation in text but cannot execute the subagent call, so the workflow continues without real delegated results.
NEW QUESTION # 57
The document-analysis agent has a single analyze_document tool that accepts 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: B
Explanation:
Option A removes the semantic ambiguity created by one generic tool performing three substantially different operations. Each replacement tool can have a distinct name, focused description, appropriate required parameters, and an output schema designed for its task. For example, extract_data_points can return typed records, while verify_claim_against_source can return a verdict, supporting excerpt, and source location.
Anthropic's tool-design guidance recommends thoughtful tools with clear, distinct purposes and natural task boundaries. Ambiguous or overlapping capabilities make it harder for agents to select the correct operation and interpret its results consistently.
Option B improves the existing interface, but the implementation and result contract remain overloaded, increasing the possibility that the model or tool will mix behavior across modes. Option C adds another probabilistic classification stage without repairing the underlying interface. Option D may reduce some errors, but free-text interpretation remains the principal control mechanism. Purpose-specific tools encode the intended operation directly into the callable interface, permit strict schemas tailored to each result type, and make evaluation failures easier to diagnose. This produces a stronger contract between the agent and deterministic document-processing system.
NEW QUESTION # 58
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