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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Tool-augmented prompting
|
| Topic 2: Claude Code Configuration & Workflows | 20% | - CI/CD and workflow integration
|
| Topic 3: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP)
|
| Topic 4: Context Management & Reliability | 15% | - System reliability
|
| Topic 5: Agentic Architecture & Orchestration | 27% | - Agentic loops & lifecycle design
|
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NEW QUESTION # 52
A developer at quicktechie.com is building a highly experimental MCP server testing a new vector database. They want to use it locally in Claude Code without exposing the configuration to the rest of the team. Which configuration file should they use?
Answer: C
Explanation:
Personal, experimental, or user-specific MCP configurations should be placed in the user-scoped -/.claude.json file, preventing them from being shared with the team via the project repository's .mcp.json.
NEW QUESTION # 53
You are building a classifier to categorize customer reviews, but the model struggles with ambiguous scenarios like sarcasm and mixed sentiments. You decide to use few-shot prompting. What is the recommended best practice for this scenario?
Answer: A
Explanation:
The optimal number of few-shot examples for ambiguous tasks is 2-4. Providing more than 6 examples bloats the prompt without adding proportional value. It is critical that at least one of these examples covers an edge case or ambiguous scenario to establish the expected reasoning pattern.
NEW QUESTION # 54
The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?
Answer: D
Explanation:
Purpose-specific tools with clear input and output contracts remove ambiguity about what kind of analysis is expected. Separate extraction, summarization, and verification tools make the agent choose the correct operation and produce outputs in the format downstream agents can reliably use.
NEW QUESTION # 55
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 # 56
A large pull request modifies 15 files. A single-pass review produces superficial comments and misses obvious cross-file bugs. How should you restructure the automated review architecture? Choose 2 correct answers.
Answer: C,D
Explanation:
Splitting large code reviews into per-file local analysis passes (to catch syntax/local logic bugs) plus a separate cross-file integration pass prevents attention dilution. Relying solely on larger context windows or requiring consensus across full monolithic runs are anti-patterns.
NEW QUESTION # 57
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