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| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Context handling
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
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NEW QUESTION # 40
A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?
Answer: B
Explanation:
Minimizing unnecessary sensitive information before sending prompts follows the principle of data minimization. Claude performs best when provided only with relevant context. Removing unrelated confidential information reduces privacy risk while preserving model performance and supporting secure enterprise AI architecture.
NEW QUESTION # 41
The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?
Answer: A
Explanation:
Defining specialized subagents is not enough; the coordinator must also be allowed to use the Task tool that actually invokes them. If Task is missing from the coordinator's allowed tools, the model may describe delegation in text but cannot execute the subagent call, so the workflow continues without real delegated results.
NEW QUESTION # 42
Your code-review prompts include both implementation changes and the corresponding test file, but the review comments fail to identify untested code paths. The model correctly flags functions that have no tests at all, but it fails to recognize when conditional branches or error-handling paths within tested functions lack coverage. What is the most effective way to improve branch- level gap detection without overcomplicating the pipeline?
Answer: A
Explanation:
The current prompt asks for testing analysis at too high a level. Claude recognizes the obvious absence of an entire test but has not been instructed to construct a path-level inventory. Option B turns the desired behavior into an explicit verification procedure: enumerate each condition, alternative branch, early return, exception handler, and failure path, then locate a test assertion that exercises its behavior.
Anthropic's prompting best practices emphasize clear, specific instructions and explicit sequential steps when a task requires a defined analysis process. This change keeps the existing single review call while making the missing evaluation criterion unambiguous.
NEW QUESTION # 43
When implementing your lookup_order MCP tool, the backend sometimes returns errors-for example,
"Order not found" or temporary database failures. What is the correct pattern for communicating these errors back to the agent?
Answer: C
Explanation:
Option A follows the MCP error contract. An error originating during execution of a valid tool call-such as an API failure, input-validation problem, or business-logic failure-should be returned inside the tool result with isError: true. The content should explain what happened and, where possible, provide actionable recovery guidance.
The official MCP tools specification distinguishes tool-execution errors from protocol-level errors and specifies that execution failures are reported through tool results using isError: true. Anthropic's tool-using agent tutorial provides the equivalent Claude API pattern using is_error: true, allowing Claude to retry with corrected input, request clarification, or explain the limitation.
Option B falsely represents a failed operation as successful, weakening reliable failure detection. Option C deprives the agent of the information required to determine an appropriate recovery action. Option D may be converted into a tool error by some SDK runners, but uncaught exceptions are not the MCP-level response contract. The strongest implementation returns a concise, sanitized, actionable error result and separately records detailed diagnostics in server logs.
NEW QUESTION # 44
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated review calls the Claude API for each pull request, using tool_use with a report_findings tool that returns a JSON array of finding objects. Each object contains file_path, line_number, severity, category, and description. During testing on a large pull request touching more than 30 files, the response reaches the max_tokens limit and is truncated in the middle of the JSON, causing your pipeline's parser to fail.
What is the most effective way to handle this?
Answer: C
Explanation:
Option A is correct because the failure results from excessive output volume, not from JSON or tool use. Anthropic documents that max_tokens is a hard output ceiling and that a response stopped at this limit reports stop_reason: "max_tokens". Its tool-streaming guidance also warns that generation can stop midway through a tool parameter, leaving partial JSON that must not be treated as complete. Splitting the pull request into bounded file groups limits the number of findings produced by each call, preserves the report_findings schema, and allows the pipeline to validate every response independently before merging and deduplicating the arrays. Shared dependency context can still be included when cross-file analysis is required.
NEW QUESTION # 45
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