最新のCCA-F問題集で試験を準備する

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.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?

• A. Consolidate semantically overlapping tools - merge issue_credit and process_refund into a single resolve_compensation tool with an action parameter, and fold check_delivery_status into lookup_order with an optional include_tracking flag.
• B. Split the tools across two sub-agents - a "financial resolution" agent with process_refund , issue_credit ,and apply_promo_code , and a "delivery operations" agent with the remaining delivery tools - with a coordinator routing between them.
• C. Add few-shot examples to the system prompt demonstrating correct selection for each ambiguous tool pair, such as showing when issue_credit applies versus when process_refund is appropriate.
• D. Enable the tool search tool with defer_loading on the six new tools, keeping the original four always loaded, so the agent dynamically discovers specialized tools only when needed.

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 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.
• B. You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
• 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 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.

A company wants Claude to summarize thousands of support tickets efficiently. Which design scales BEST?

• A. Remove system prompts.
• B. Increase temperature.
• C. Process tickets individually and combine summaries.
• D. Send all tickets in one request.

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
A user is expanding the research system beyond its single web search agent by adding specialized data sources. They add a financial API agent that returns structured JSON with revenue, margins, and growth rates; a news monitoring agent that returns prose summaries of recent developments; and a patent analysis agent that returns structured lists of technology areas. The synthesis agent combines these into executive briefings. Currently, it converts everything to bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose narrative flow. What change would most improve briefing quality?

• A. Standardize all subagent outputs to prose summaries with inline citations
• B. Standardize all subagent outputs to JSON with fields for claim, evidence, source, and confidence
• C. Add a format conversion layer between subagents and synthesis that transforms all outputs to a common intermediate representation
• D. Update the synthesis agent to render each content type appropriately - financial data as tables, news as prose

After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

• A. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
• B. Pass the full accumulated context from all prior agents.
• C. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
• D. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.