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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Context pruning and summarization strategies - Context window optimization and prioritization - Idempotency, consistency and failure resilience - Token budget management and cost control |
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation - Error handling and tool response formatting - Tool schema design and interface boundaries - Tool distribution and permission controls |
| Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules |
| Agentic Architecture & Orchestration | 27% | - Session state management and workflow enforcement - Error recovery, guardrails and safety patterns - Agentic loop design and stop_reason handling - Task decomposition and dynamic subagent selection - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting - JSON schema design and structured output enforcement |
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NEW QUESTION # 149
You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
Your process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (~5% of calls), and business errors ("Order exceeds 30-day return window", "Item already refunded") that are permanent (~12% of calls). Monitoring shows the agent wastes 3-
4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude.
What's the most effective way to reduce wasted retries while improving customer-facing response quality?
Answer: D
Explanation:
The error response must explicitly communicate both control semantics and user-facing meaning. Setting
"retriable": false tells the agent that repeating the same operation cannot change the outcome. Providing a customer-friendly explanation allows Claude to respond accurately without exposing internal implementation details or inventing its own interpretation of the business rule.
Anthropic recommends returning failed tool operations with an error indicator and sufficiently informative content so Claude can decide whether to retry, request another input, or explain the limitation. Tool interfaces should provide detailed descriptions and structured parameters rather than forcing Claude to infer operational behavior from ambiguous text. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/build-a-tool- using-agent?utm_source=chatgpt.com ) Option A appropriately limits automatic retries to technical failures, but it does not solve the stated customer- response problem unless business failures also carry clear semantics. Option B relies on fragile parsing of human-readable messages. Option C adds latency and another tool dependency, and eligibility may still change or fail for reasons not covered by the preliminary check.
A complete schema should identify the error code, category, retryability, customer-safe explanation, and permitted next actions. Technical errors can similarly return "retriable": true with retry guidance and a maximum-attempt policy.
Official references/topics: Structured tool errors, retryability classification, customer-safe explanations, resilient MCP contracts.
NEW QUESTION # 150
Your test-generation process produces unit tests for new code, but reviews show that 55% are low-value: trivial assertions that verify only that functions do not throw exceptions, tests that duplicate existing coverage, or tests that ignore your team's fixture conventions. How should you reduce the rate of low-value tests being generated in the first place?
Answer: D
Explanation:
The failures reflect missing project-specific knowledge: Claude does not know which fixtures are preferred, what the existing suite already covers, or what the team considers meaningful behaviour. Option C provides this information as persistent project context before test generation begins. This changes generation quality at the source instead of filtering weak tests after spending tokens to produce them.
Anthropic's CLAUDE.md documentation recommends storing shared build and test commands, coding standards, architectural decisions, conventions, and common workflows in a project CLAUDE.md. Testing guidance can define required behavioural assertions, fixture selection rules, duplication checks, naming conventions, and representative examples of acceptable and unacceptable tests.
NEW QUESTION # 151
Which statement BEST describes Retrieval-Augmented Generation?
Answer: B
NEW QUESTION # 152
An application frequently repeats the same background instructions in every API request. What is a potential downside?
Answer: C
Explanation:
Repeated instructions consume additional input tokens on every request, increasing cost and latency. Consolidating stable guidance into reusable prompt templates can improve efficiency while preserving consistent model behavior.
NEW QUESTION # 153
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: C
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 # 154
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