Anthropic : CCA-F Questions & Answers

A customer returns 4 hours after the initial session about the same billing dispute. The previous
32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution:
24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information. What approach most reliably handles returning customers?

• A. Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.
• B. Resume with full history and configure the agent to automatically re-call all previously-used tools at session start to ensure data freshness.
• C. Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.
• D. Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.

A developer includes multiple unrelated tasks inside one extremely long prompt. What is the MOST likely outcome?

• A. Faster inference.
• B. Improved reasoning.
• C. Increased prompt clarity.
• D. Reduced instruction clarity.

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.
A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers. What task decomposition approach would be most effective?

• A. Run parallel worker agents that simultaneously investigate all four layers, then synthesize their findings to identify where the error originates.
• B. Define a fixed sequence of investigation steps upfront - grep for error patterns, then read error handlers, then check database queries, then examine middleware - executing each step regardless of intermediate findings.
• C. Have the agent dynamically generate investigation subtasks based on what it discovers at each step, adapting its exploration plan as new information about the error path emerges.
• D. Have the agent first create a comprehensive plan mapping all code paths through the endpoint before beginning any file exploration or code reading.

After the web search and document analysis subagents complete their tasks, the coordinator needs to spawn the synthesis subagent to synthesize the findings. What is the correct approach for providing the synthesis subagent with the information it needs?

• A. Include the complete findings from both subagents directly in the synthesis subagent's prompt
• B. Pass reference identifiers and configure the subagent with read access to a shared memory store where other subagents deposited their results
• C. Provide the subagent with tool definitions that allow it to request outputs from other subagents via callbacks
• D. Spawn the subagent with only a brief task description, relying on automatic context inheritance from the coordinator

After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?

• A. The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.
• B. The subagents need to share a single API connection to enable automatic context sharing between invocations.
• C. The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.
• D. The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.