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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Context Management & Reliability | 15% | - Context handling
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
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NEW QUESTION # 185
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.
After implementing tool use with strict schema definitions, JSON syntax errors are eliminated, but 5% of extractions still contain empty arrays or null values for required fields such as citations and methodology.
Spot-checking reveals that the source documents contain this information, but in varied formats-inline citations versus bibliographies, and methodology sections versus details embedded in introductions.
What is the most effective way to address these failures?
Answer: D
Explanation:
Option B targets the remaining failure mode: semantic recognition across heterogeneous document structures.
Strict schemas eliminate malformed JSON and can guarantee that tool inputs conform to declared types, but they cannot force Claude to locate evidence that appears under unfamiliar headings or in atypical sections.
Anthropic's prompting guidance says that a few relevant, diverse, structured examples are among the most reliable ways to improve accuracy and consistency. Examples should therefore show inline citations, reference lists, numbered bibliographies, methodology sections, and methods embedded in introductions, each paired with the correct extracted structure. This teaches the intended evidence-location and granularity rules rather than merely repeating the same request. Option A retries an unchanged prompt and can reproduce the same omission. Option C introduces brittle regex rules that may miss nonstandard citations and mistake keyword mentions for methodology content. Option D suppresses validation failures by weakening the contract, but it does not improve extraction and would convert recoverable omissions into incomplete records.
The examples should be drawn from real failure cases, evaluated on a held-out set, and expanded when monitoring reveals new layouts. Schema constraints and few-shot coverage solve different layers of reliability and should be used together.
NEW QUESTION # 186
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: C
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.
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.
NEW QUESTION # 187
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 coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate-it generates messages such as, "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: C
Explanation:
Option C matches the distinction between reasoning about delegation and executing it. Defining subagents makes their descriptions available for selection, but the coordinator must still invoke the SDK's subagent- spawning tool. Current Claude Agent SDK documentation calls this the Agent tool; Task was its earlier name and remains relevant to older SDK configurations. Anthropic's Subagents in the SDK documentation instructs developers to include Agent in allowedTools so subagent invocations are approved automatically. Without that permission, an invocation can fall through to a permission callback or be denied under a non-interactive permission mode. Option A is unlikely because the configured subagent descriptions already tell Claude when each agent should be selected, although explicit prompting can improve invocation reliability. Option B misstates context isolation: context must be included in the spawning prompt, but that issue occurs after an invocation is attempted and does not explain the absence of all subagent executions. Option D would normally produce truncation evidence or incomplete output rather than consistent verbal promises with no tool call. The configuration should therefore permit Agent, explicitly request delegation where necessary, and log subagent invocation events.
NEW QUESTION # 188
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at
82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to
68%.
How should you address this trade-off to improve detection across both categories?
Answer: D
Explanation:
Option A separates competing objectives so each review call can devote its attention and examples to a coherent concern. Anthropic's Building Effective AI Agents describes sectioning as a parallelization pattern in which independent aspects are handled by separate model calls and then aggregated. It specifically notes that models generally perform better on complex tasks when each consideration receives focused attention. The evaluation results demonstrate prompt interference: optimizing business-logic detection reduces API-design recall.
A security and API-design reviewer can use examples, terminology, and evidence criteria appropriate to interfaces and vulnerabilities. A business-logic reviewer can focus on state transitions, arithmetic boundaries, invariants, and domain-specific edge cases. Their structured findings can then be deduplicated and ranked before posting.
NEW QUESTION # 189
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.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match - the file has repetitive docstrings, variable names, and structural patterns.
What's the most reliable way to complete this insertion?
Answer: D
Explanation:
When Edit cannot identify a unique match, rewriting the fully read file is the most reliable way to control the exact insertion point. The other options are brittle, place the function incorrectly, or risk unintended replacements.
NEW QUESTION # 190
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