Learn with Anthropic : CCAR-F training material for 100% pass

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.
In production, you observe that simple fact-checking queries, such as "In what year was the Paris Climate Agreement signed?", traverse all four subagents sequentially, consuming more than 40 seconds and significant tokens per query. Complex comparative research benefits from the complete pipeline. Your query distribution is diverse and continues to evolve as users discover new applications.
What is the most effective approach to optimize for varying query complexity?

• A. Create a fast path for factual questions that bypasses subagents entirely, routing every other query through the complete pipeline.
• B. Have the coordinator analyze each query and dynamically determine which subagents are required.
• C. Train a query-complexity classifier using labeled historical data to predict the optimal subagent combination, retraining it periodically.
• D. Implement pattern-based routing that classifies queries as single-fact, comparative, or analytical and maps each category to a predefined subagent combination.

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

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

Your multi-agent research pipeline crashed after processing12 of 28 documents. The web search agent had identified relevant sources, the document analyzer had partially completed extraction, and the synthesizer had begun pattern identification. You need to resume processing without repeating work or losing fidelity of prior findings. What state management approach best balances information fidelity with context efficiency when restoring agent state?

• A. Have each agent persist a structured export to a known location. On resume, the coordinator loads the manifest and injects relevant state into agent prompts.
• B. Persist the coordinator's conversation log containing all task delegations and responses, providing this to agents when resuming.
• C. Index all agent outputs in a shared vector store. When resuming, each agent queries the store using semantic search to retrieve relevant prior findings.
• D. Have each agent maintain its own persistent state file and reload it independently at the start of each session.

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

Your test-generation process produces unit tests for new code, but reviews show that 55% are low-value: trivial assertions that verify only that functions do not throw exceptions, tests that duplicate existing coverage, or tests that ignore your team's fixture conventions. How should you reduce the rate of low-value tests being generated in the first place?

• A. Restrict test generation to directories where historical quality metrics show higher acceptance rates, disabling it in areas where generated tests consistently require substantial editing.
• B. Implement two-phase generation in which a second Claude call scores every test against quality criteria and filters out low-scoring tests before presenting them to developers.
• C. Document your testing standards in CLAUDE.md, including valuable-test criteria, available fixtures and their intended uses, and examples distinguishing meaningful behavioural tests from trivial assertions.
• D. Add post-generation coverage analysis that automatically filters out every generated test that does not increase line coverage beyond the existing test suite.