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| Section | Weight | Objectives |
|---|---|---|
| Claude Code Configuration & Workflows | 20% | - CI/CD integration and non-interactive mode parameters - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool distribution and permission controls - Error handling and tool response formatting - MCP tool, resource and prompt implementation - Tool schema design and interface boundaries |
| Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Task decomposition and dynamic subagent selection |
| Context Management & Reliability | 15% | - Token budget management and cost control - Idempotency, consistency and failure resilience - Context window optimization and prioritization - Context pruning and summarization strategies |
| Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - System prompt design and persona alignment - Explicit criteria definition and few-shot prompting |
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NEW QUESTION # 91
Your MCP server implements a check_availability tool that queries an external calendar API.
During testing, you encounter three error conditions: (1) the tool is called with a malformed request missing the required user_email parameter, (2) the calendar API returns a 404 because the specified user doesn't exist in the calendar system, and (3) the calendar API returns a 503 because the service is temporarily unavailable. How should each error be reported according to MCP's error handling design?
Answer: D
Explanation:
Errors caused by malformed requests (missing required parameters) violate the RPC protocol and should be reported as JSON-RPC errors. Errors returned by the external service (user not found, temporary unavailability) are valid tool execution results and should be reported as tool outputs with isError set to true, allowing the agent to handle them gracefully.
NEW QUESTION # 92
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.
An engineer who recently joined the team asks the agent to explain the authentication and authorization architecture before making security improvements. The codebase contains more than 800 files across multiple services.
What exploration strategy will most effectively build understanding while respecting context limits?
Answer: A
Explanation:
Option D builds an evidence-based architectural map without indiscriminately loading hundreds of files. Authentication entry points may include route handlers, middleware registration, token- validation functions, session constructors, or identity-provider callbacks. After Grep identifies those anchors, targeted Read operations can establish their responsibilities, while imports, call sites, and configuration references reveal the downstream authorization flow. Anthropic's large- codebase guidance recommends scoping Claude to the portion of a repository touched by the task because unrelated instructions and file reads consume tokens and degrade performance. Its common workflows guidance similarly recommends beginning broadly and narrowing into specific components.
NEW QUESTION # 93
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.
You're tasked with adding real-time updates to the application. This could be implemented using WebSockets, Server-Sent Events, or polling, each with different complexity, browser support, and infrastructure requirements.
What's the most effective way to begin this task?
Answer: A
Explanation:
The implementation approach is unresolved and carries architectural consequences. WebSockets, Server-Sent Events, and polling differ in connection lifecycle, bidirectional communication, proxy compatibility, scaling requirements, deployment topology, reconnection behavior, and operational complexity. Claude should therefore inspect the existing application and infrastructure before modifying code.
Anthropic recommends separating exploration and planning from implementation when the approach is uncertain, the work affects multiple files, or the developer is unfamiliar with the relevant architecture. In plan mode, Claude can read files and analyze the system without making changes, produce a detailed proposal, and allow the developer or team to review the plan before execution. ( https://code.claude.com/docs/en/best- practices ) Option A commits to polling before determining whether its latency and load characteristics meet the requirement. Option B delegates a consequential architectural decision and immediate implementation to a single execution step without a review checkpoint. Option D prematurely selects the most infrastructure- sensitive option and assumes later refactoring will be inexpensive.
Option C creates the correct sequence: examine the frontend and backend architecture, document requirements, compare alternatives, identify infrastructure constraints, obtain approval, and only then implement the selected design with appropriate tests and observability.
Official references/topics: Plan mode, explore-plan-implement workflow, architectural trade-off analysis, review checkpoints.
NEW QUESTION # 94
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 must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON.
What approach provides the most reliable schema compliance?
Answer: D
Explanation:
A tool definition converts the desired extraction structure into an explicit machine-readable contract. Claude returns the event information inside a tool_use block, with the tool arguments corresponding to the properties defined by the tool's input_schema . Anthropic specifies that custom tool parameters are described using JSON Schema, allowing the application to extract the structured arguments directly rather than attempting to recover JSON from ordinary prose. For current implementations, adding strict: true to the tool definition provides guaranteed conformance of tool-call inputs to the declared schema. ( https://docs.anthropic.com/en
/docs/agents-and-tools/tool-use/implement-tool-use )
Options A, B, and D remain prompt-based formatting techniques. They may improve the probability of valid JSON, but none creates the same schema-enforced interface. Prefilling an opening brace constrains the beginning of the response without guaranteeing valid field names, required properties, or data types. Retry logic detects failures only after generation and adds latency. Detailed formatting instructions can still produce malformed or structurally incorrect output.
Anthropic now also provides Structured Outputs for direct, schema-validated JSON responses. Within the options presented, however, a schema-defined tool is the only approach that establishes an explicit structured- output boundary rather than relying primarily on text-generation compliance. ( https://docs.anthropic.com/en
/docs/test-and-evaluate/strengthen-guardrails/increase-consistency )
Official references/topics: Tool Definitions, JSON Schema Input Contracts, Strict Tool Use, Structured Outputs.
NEW QUESTION # 95
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 operating with 100% human review for 3 months. Analysis shows that extractions with model confidence #90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high- confidence extractions.
Before deploying, what validation step is most critical?
Answer: D
Explanation:
An aggregate accuracy value can conceal severe performance disparities. A system may achieve 97% overall accuracy while performing poorly on a low-volume document type, a critical financial field, or a specific edge case. Automating outputs solely from the aggregate figure could therefore expose downstream systems to concentrated, high-impact errors.
Anthropic's evaluation guidance states that evaluations should be task-specific, reflect the real-world task distribution, and explicitly include edge cases. It also emphasizes multidimensional success criteria rather than reliance on a single global metric. ( https://docs.anthropic.com/en/docs/build-with-claude/develop-tests ) Option A applies those principles by stratifying performance according to document type and field. This reveals whether confidence is calibrated consistently and whether the proposed automation threshold remains safe for every operationally significant segment.
Option B is useful only after segment-level performance has been understood. Selecting a global threshold cannot correct a subgroup where confidence is systematically overstated. Option C is necessary governance work, but it treats the overall 97% result as though errors were uniformly distributed. Option D places unvalidated outputs into downstream systems and depends on passive error reporting, which may fail to detect silent corruption.
The correct deployment gate is therefore segmented validation, followed by threshold selection, downstream acceptance criteria, and a controlled pilot.
Official references/topics: Define Success Criteria; Task-Specific Evaluations; Edge-Case Coverage; Reliability Segmentation.
NEW QUESTION # 96
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