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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Context Management & Reliability | 15% | - Context handling
|
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NEW QUESTION # 136
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.
An engineer asks the agent to find all callers of a function before removing it. The function is defined in a core library but is also exposed through wrapper modules that rename the function for domain-specific use (e.
g., calculateTax in the library becomes computeOrderTax in the orders module).
What exploration strategy will most reliably identify all callers?
Answer: C
Explanation:
The search must first establish the function's complete naming surface. Reading the core definition and every wrapper or re-export reveals aliases such as computeOrderTax , names introduced through intermediate modules, and potentially different import paths. Once those names are known, the agent can search the entire codebase for each exposed identifier and inspect the resulting call sites.
Claude Code's Grep tool searches patterns inside file contents, whereas Read provides the surrounding implementation needed to determine whether a wrapper delegates to, renames, or conditionally invokes the original function. These tools are complementary: Read establishes semantic identity; Grep provides broad reference discovery. ( https://code.claude.com/docs/en/tools-reference?utm_source=chatgpt.com ) Option B misses every caller using a wrapper alias. Option A may find importing files, but import-level discovery is indirect and can miss re-exports, namespace imports, dependency injection, or calls made through a locally renamed symbol. It also requires unnecessary manual inspection of every consumer of the module. Option D relies on documentation, which may be incomplete or stale and cannot prove that all executable references have been found.
Before removal, the agent should also inspect tests, dynamic registrations, configuration-driven references, and generated code where relevant. The defining requirement is to map the alias chain first and then search all discovered public names.
Official references/topics: Read and Grep Tool Behavior; Symbol and Alias Discovery; Codebase Reference Tracing.
NEW QUESTION # 137
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: D
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 # 138
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.
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API.
What is the primary mechanism your application uses to determine whether to continue the loop or stop?
Answer: C
Explanation:
The Messages API exposes stop_reason specifically so the application can determine why Claude stopped generating and what action is required next. A value of tool_use means Claude is requesting one or more tools and expects the application to execute them and return corresponding tool_result blocks. A value of end_turn means Claude has naturally completed the response. ( https://platform.claude.com/docs/en/build-with-claude
/handling-stop-reasons )
Anthropic's agent-loop tutorial implements this as a loop that continues executing tools and returning results while stop_reason remains tool_use . Once the value changes, the response is treated according to the new terminal or continuation condition. ( https://platform.claude.com/docs/en/agents-and-tools/tool-use/build-a- tool-using-agent ) Option A is unreliable because an assistant response can contain both text and tool-use blocks. Text alone does not prove that the workflow is complete. Option B requires the application to predict that no further tool call is needed, defeating Claude's ability to adapt from tool results. Option D is an important safety limit but is not the primary completion signal; an arbitrary maximum may terminate a valid workflow prematurely.
The harness should check stop_reason , process every requested tool call, append results to the conversation, and maintain a separate maximum-turn safeguard against runaway loops.
Official references/topics: Messages API stop reasons, tool-use loops, tool-result continuation, bounded orchestration.
NEW QUESTION # 139
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.
An engineer submits two requests:
- Request A: "Rename the getUserData function to fetchUserProfile
everywhere it's used."
- Request B: "Improve error handling throughout the data processing
module - add try/catch blocks, meaningful error messages, and ensure
failures don't silently corrupt data."
For which request does specifying an explicit multi-phase workflow (such as analyze -> propose
-> implement with review) most improve outcome quality?
Answer: B
Explanation:
Improving error handling is broad and ambiguous, requiring analysis of failure paths, design decisions, and review before implementation. A simple function rename is deterministic and can usually be executed directly.
NEW QUESTION # 140
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: C
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 # 141
......
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