最も有効な CCA-F 試験参考書

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?

• A. You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
• B. You check whether Claude's response contains a text content block - if text is present, the agent has produced its final answer and the loop should exit.
• C. You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
• D. You check the stop_reason field in each API response - the loop continues while it equals
  "tool_use" and exits when it changes to "end_turn" or another terminal value.

Claude produces occasional factual inaccuracies despite clear prompts. Which architectural enhancement is MOST effective?

• A. Remove examples.
• B. Add grounding through trusted retrieval.
• C. Shorten responses only.
• D. Increase temperature.

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 sees an unfamiliar error message "SYNC_CONFLICT: entity version mismatch detected" in production logs but doesn't know which of the 12 services in the codebase generates it. They ask the agent to help locate the source code. What exploration approach will most efficiently find the responsible code?

• A. Use Glob to find files in directories commonly associated with error handling (such as errors/, exceptions/,or handlers/) across all services, then Read each matching file.
• B. Use Grep to search for distinctive text from the error message (like "SYNC_CONFLICT" or "entity version mismatch"), then Read the matching files to understand context.
• C. Use Grep to find all files that import the project's error handling module, then Read those files to locate custom error definitions.
• D. Read the project's README and service configuration files to understand the architecture, then systematically Read source files in each service directory.

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 used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth - having the agent propose specific code changes for each - before deciding which to implement. What's the most effective way to structure this exploration?

• A. Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
• B. Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
• C. Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
• D. Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.

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?

• A. Accept this as expected behavior since simpler tools like sed are more predictable than specialized refactoring tools.
• B. Implement a request classifier that detects refactoring intent and automatically routes those requests to the MCP server before the agent processes them.
• C. Enhance the MCP tool descriptions to explain when each tool is preferable to text manipulation and clarify expected inputs and outputs.
• D. Remove the Write tool from the agent's configuration for refactoring sessions so it must use the MCP tools for code modifications.