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| Section | Weight | Objectives |
|---|---|---|
| Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Context Management & Reliability | 15% | - Context window optimization and prioritization - Token budget management and cost control - Context pruning and summarization strategies - Idempotency, consistency and failure resilience |
| Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Error handling and tool response formatting - Tool distribution and permission controls - Tool schema design and interface boundaries |
| Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - CLAUDE.md hierarchy, precedence and @import rules - CI/CD integration and non-interactive mode parameters |
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment - Validation, parsing and retry loop strategies |
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NEW QUESTION # 162
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: A
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 # 163
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 # 164
In production, final reports frequently contain claims without proper source attribution.
Investigation shows that while the web search and document analysis agents correctly attach citations to their outputs, the synthesis agent loses track of which sources support which conclusions when combining findings. What's the most effective architectural change?
Answer: D
Explanation:
Structured claim-to-source mappings ensure that the synthesis agent can merge findings without losing attribution. By preserving these explicit links through the workflow, the final report can include accurate citations for all claims, maintaining reliability and traceability.
NEW QUESTION # 165
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.
Your multi-agent research pipeline crashes after processing 12 of 28 documents. The web-search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun identifying patterns. You need to resume processing without repeating work or losing fidelity in the prior findings.
What state-management approach best balances information fidelity with context efficiency when restoring agent state?
Answer: B
Explanation:
Option B combines lossless persistence with selective restoration. Each agent should checkpoint structured records containing completed document IDs, extracted findings, source metadata, unresolved work, validation status, and schema version. A coordinator-level manifest records where those artifacts are stored and which pipeline stages completed. On resumption, the coordinator can skip the 12 completed documents and inject only the artifacts relevant to each restarted agent. Anthropic's effective context-engineering guidance recommends structured note-taking outside the context window so agents can preserve project state across resets while loading only the information needed later. A vector store, option A, is useful for relevance retrieval but can omit exact details through similarity-based selection and is unsuitable as the sole recovery record. Option C fragments ownership of recovery state and makes cross-agent consistency difficult to verify.
Option D preserves an extensive trace, but replaying a complete conversation wastes context and mixes obsolete instructions, intermediate reasoning, and redundant outputs. Structured checkpoints plus a manifest provide deterministic recovery, auditability, exact provenance, and efficient prompt reconstruction.
Checkpoints should be written atomically and validated before the corresponding stage is marked complete.
NEW QUESTION # 166
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
After adding an MCP server with specialized code-refactoring tools-extract_function, rename_variable, and inline_function-you notice that the agent still uses basic text manipulation through Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy. Examining the configuration, you find that each MCP tool has a minimal description such as, "extract_function: Extracts a function from code." What is the most effective way to improve adoption of the MCP refactoring tools?
Answer: B
Explanation:
Option C corrects the weak selection signal presented to the model. Claude chooses among available tools using their names, descriptions, parameter schemas, and the current request. "Extracts a function from code" does not explain whether the tool understands syntax trees, preserves imports, updates call sites, validates scope, or offers advantages over Write and sed. Anthropic identifies prompt-engineering tool descriptions as one of the most effective ways to improve agent tool use. Descriptions should state what the operation performs, when it should be selected, what inputs are required, what output it returns, and any limitations.
Option A adds a separate probabilistic routing layer without improving the tool contract Claude ultimately sees. Option B ignores the server's intended value. Option D removes a broadly useful capability and may prevent unrelated edits without guaranteeing that the MCP tools are used correctly. Each refactoring tool should instead describe its semantic behavior and contrast it with plain text manipulation-for example, that rename_variable performs scope-aware symbol renaming and updates references. Clear schemas, concrete examples, and evaluation against real refactoring tasks should accompany the improved descriptions.
NEW QUESTION # 167
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