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| Section | Weight | Objectives |
|---|---|---|
| Tool Design & MCP Integration | 18% | - Tool schema design and interface boundaries - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Error handling and tool response formatting - Tool distribution and permission controls - MCP tool, resource and prompt implementation |
| Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Path-specific rules and .claude/rules/ configuration |
| Prompt Engineering & Structured Output | 20% | - Validation, parsing and retry loop strategies - JSON schema design and structured output enforcement - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting |
| Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns |
| Context Management & Reliability | 15% | - Token budget management and cost control - Idempotency, consistency and failure resilience - Context pruning and summarization strategies - Context window optimization and prioritization |
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NEW QUESTION # 30
Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output. How should you modify the system to run these subagents concurrently?
Answer: D
Explanation:
Option A exposes both independent invocations in the same assistant turn, allowing the Agent SDK or application tool runner to execute them concurrently. The coordinator can then receive both results together and continue with synthesis only after the independent research branches have completed.
Anthropic's parallel tool-use documentation explains that a response may contain multiple tool-use blocks.
Independent, read-only operations can be executed concurrently to reduce latency, after which all corresponding tool results should be returned together. The term "Agent" is used here because current Claude Agent SDK releases renamed the earlier "Task" tool.
Option B may shorten individual execution but does not eliminate the sequential waiting pattern and could reduce research quality. Option C expresses the desired behavior but does not correct an orchestration implementation that processes only one tool call per turn. Option D introduces unnecessary coordinators, duplicated context, and substantially more complex state management. A single coordinator issuing both independent Agent calls preserves centralized monitoring and result association while removing the avoidable serial dependency. The runtime must process every returned tool call concurrently rather than stopping after the first one.
NEW QUESTION # 31
You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
Your team frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing /migrate-component. The workflow should stay in sync as the team iterates on it. Where should you place the skill file?
Answer: D
Explanation:
A project-scoped skill is available to everyone working in that repository and stays synchronized through version control. The skill directory name exposes it as /migrate-component.
NEW QUESTION # 32
After the web search and document analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?
Answer: C
Explanation:
The synthesis subagent requires the actual content from prior subagents to perform its task.
Including the complete findings in its prompt ensures it has the necessary context to accurately synthesize information without relying on indirect references or external state.
NEW QUESTION # 33
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 system has been running for 3 weeks and human reviewers have corrected 847 extractions. Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or "a splash," the model either invents specific amounts or leaves fields empty-accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?
Answer: C
Explanation:
The reviewer corrections have exposed a narrow and repeatable interpretation failure. The desired policy is clear: informal measurements are valid source values and must be preserved verbatim rather than normalized into invented quantities or treated as missing. This behavior can be communicated efficiently through targeted few-shot examples.
Anthropic recommends examples for demonstrating expected behavior and improving consistency. Examples can pair source phrases such as "a handful of spinach," "a splash of vinegar," and "a pinch of salt" with outputs that retain handful , splash , and pinch exactly. Additional counterexamples can show that Claude must not convert these phrases into grams, millilitres, or estimated serving quantities. ( https://docs.anthropic.
com/en/docs/about-claude/use-case-guides/ticket-routing )
Option A introduces a substantially heavier training workflow for a problem that can be addressed directly through the prompt. Option C creates a parallel extraction mechanism based on pattern matching; it will be brittle across linguistic variations and may populate a value without understanding its relationship to the correct ingredient. Option D adds useful classification metadata, but it does not instruct Claude to preserve the original measurement instead of inventing or omitting it.
The revised prompt should combine an explicit verbatim-extraction rule with several varied examples derived from the corrected cases, followed by regression evaluation against the identified failure set.
Official references/topics: Few-Shot Examples, Feedback-Driven Prompt Improvement, Verbatim Extraction, Regression Evaluation.
NEW QUESTION # 34
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 extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30- day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?
Answer: C
Explanation:
The document contains multiple factually valid values whose applicability depends on chronology and legal context. Collapsing those values into a single scalar field discards essential provenance. Option B corrects the data model by representing each term as a structured record containing the extracted value, source location, document or amendment identifier, and effective date.
Anthropic's Structured Outputs feature is designed for data-extraction use cases in which nested objects and arrays must conform to a defined JSON Schema. Anthropic also recommends grounding factual outputs in direct source material and making claims auditable through supporting evidence. A provenance-aware schema applies both principles: it retains the original clause and the amendment instead of forcing Claude to resolve a potentially complex legal precedence question during extraction.
NEW QUESTION # 35
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