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 (e.g., "What year was the Paris Climate Agreement signed?") traverse all four subagents sequentially, consuming 40+ seconds and significant tokens per query. Complex comparative research benefits from the full pipeline.
Your query distribution is diverse and evolving as users discover new applications. What's the most effective approach to optimize for varying query complexity?

• A. Have the coordinator analyze each query and dynamically decide which subagents to invoke based on its assessment of query requirements.
• B. Train a query complexity classifier on labeled historical data to predict optimal subagent combinations, retraining periodically as query patterns evolve.
• C. Create a fast-path for factual questions that bypasses subagents entirely, routing all other queries through the complete pipeline to ensure research thoroughness.
• D. Implement pattern-based routing that categorizes queries by structure (single-fact vs.
  comparative vs. analytical) and maps each category to a predefined subagent combination.

The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate--it generates messages such as, "I'll ask the web-search agent to find sources on this topic"--but no subagent execution occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?

• A. The coordinator's max_tokens setting is too low, causing the subagent invocation to be truncated before the agent-type parameter is specified.
• B. Subagent context isolation prevents task descriptions from reaching subagents unless explicit context forwarding is configured in ClaudeAgentOptions.
• C. The AgentDefinition objects are configured correctly, but the coordinator's system prompt does not explicitly list the available subagent types.
• D. The coordinator's allowedTools configuration does not include "Agent"--called "Task" in older SDK releases--so it cannot invoke the tool required to spawn subagents.

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.
Your agent needs to insert a new helper function into the middle of a 150-line utility module, between two existing functions. The Edit tool fails because its old_string parameter cannot find unique text to match - the file has repetitive docstrings, variable names, and structural patterns.
What's the most reliable way to complete this insertion?

• A. Use Bash to append the function definition to the end of the file using heredoc syntax
• B. Use Read to load the file, add the function at the appropriate location, then Write the updated file
• C. Use Edit's replace_all parameter to target a common pattern and embed the new function in the replacement text
• D. Use Edit with an extremely long old_string capturing 30+ lines of context to guarantee uniqueness

The automated review consistently flags patterns your team uses intentionally--force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK.
Developers are dismissing approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?

• A. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.
• B. Configure the review to analyze only the changed lines in the diff without surrounding file context, reducing the amount of code the model evaluates during each review.
• C. Build post-processing keyword filters that suppress findings containing terms such as "force unwrap," "large class," or "deprecated import" before results reach developers.
• D. Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.

During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process _refund immediately - but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist. Which change directly addresses the root cause of the agent fabricating the order_id value?

• A. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
• B. Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
• C. Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
• D. Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.