最も有効な CCAR-F 試験参考書

Your automated review generates many findings per pull request, but developer feedback shows that roughly half are dismissed as "not worth addressing." Analysis reveals that dismissed findings are often technically accurate but involve minor style preferences or patterns that are acceptable in your codebase. Before adding infrastructure complexity, what prompt-design change could most effectively reduce dismissals while maintaining the detection of genuine issues?

• A. Implement a secondary classification model that filters Claude's findings according to predicted developer acceptance.
• B. Ask Claude to rate every finding's confidence from 1 to 10 and include only findings rated 8 or higher.
• C. Add explicit criteria defining which issues to report, such as bugs and security defects, and which issues to skip, such as minor style preferences and accepted local patterns.
• D. Append instructions telling Claude to "only report findings you are highly confident are genuine problems."

解説: (ShikenPASS メンバーにのみ表示されます)

Your infrastructure-as-code repository includes Terraform modules (/terraform/), Kubernetes manifests (/kubernetes/), and CI/CD pipeline scripts (/pipelines/). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?

• A. Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*"]), loading rules only when editing matching files.
• B. Split content into subdirectory CLAUDE.md files (/terraform/CLAUDE.md,
  /kubernetes/CLAUDE.md), so Claude loads directory-specific guidance.
• C. Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g, "## Terraform Conventions"), improving organization and readability.
• D. Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.

解説: (ShikenPASS メンバーにのみ表示されます)

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.

解説: (ShikenPASS メンバーにのみ表示されます)

Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?

• A. Return is_error: true with a message encouraging retry: "Delivery service temporarily unavailable.
  Please retry the notification."
• B. Return is_error: true with the original message content echoed back.
• C. Return is_error: true with a structured field retry_safe: true for timeouts, distinguishing them from permanent failures that should not be retried.
• D. Return is_error: true with a message communicating uncertainty: "Timeout - status unknown.
  Message may have been sent. Avoid retry."

解説: (ShikenPASS メンバーにのみ表示されます)

The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?

• A. The coordinator agent receives the web search agent's output and includes relevant findings in the prompt when invoking the document analysis agent.
• B. The agents communicate through an event-driven message queue, with the document analysis agent subscribing to web search completion events.
• C. Both agents access a shared memory store where the web search agent writes findings and the document analysis agent reads them.
• D. The web search agent directly invokes the document analysis agent, passing the discovered sources as parameters.

解説: (ShikenPASS メンバーにのみ表示されます)