A financial services company plans to integrate Claude into an internal document analysis platform. The architects want to minimize exposure of confidential client information while maintaining high-quality responses. Which approach BEST aligns with Anthropic's recommended architecture?

• A. Send every document without preprocessing to maximize context.
• B. Replace Claude with a smaller local model regardless of task complexity.
• C. Remove unnecessary sensitive information before sending prompts whenever possible.
• D. Store every prompt permanently for auditing.

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. 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.
• B. Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
• 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. Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.

Your content curation agent discovers articles, analyzes each for relevance, then adds selected articles to themed collections. With separate discover_articles(topic), analyze_article(id), and add_to_collection(article_id, collection_id) tools, you observe 18+ sequential tool calls per request, causing latency issues. The agent must make editorial judgments about which articles fit a collection's theme - this requires seeing all candidates with their analysis scores simultaneously to select a cohesive set. What tool composition best addresses efficiency while preserving editorial judgment?

• A. Create a curate_collection(topic, collection_id) tool that handles discovery, analysis, and selection internally using configurable quality thresholds.
• B. Keep all tools separate but implement response caching for analyze_article calls.
• C. Create a discover_and_analyze(topic) composite tool that returns all candidates with their analysis scores, keeping add_to_collection separate for selective calls.
• D. Add a preview_curation(topic, collection_id) tool that shows what would be added based on predefined rules, with an approve_curation() tool to confirm.

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You've asked Claude to write a data migration script, but the initial output doesn't correctly handle records with null values in required fields. What's the most effective way to iterate toward a working solution?

• A. Provide a test case with example input containing null values and the expected output, then ask Claude to fix it.
• B. Describe the null value problem in detail and ask Claude to regenerate the entire script with improved edge case handling.
• C. Add "think harder about edge cases" to your prompt and request a complete rewrite of the migration logic.
• D. Manually edit the generated code to fix the null handling, then continue working with Claude on other parts.

Which prompt revision is MOST likely to improve output quality?

• A. Include objective, context, constraints, and expected format.
• B. "Help me."
• C. "Summarize this."
• D. Use only uppercase letters.