Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?

• A. Add more tools to cover every variation of the requests the agent handles, on the grounds that more tools give the agent more accurate options to choose from.
• B. Restructure the tool set by consolidating overlapping tools, removing unused tools, and clarifying tool descriptions so each tool has a distinct purpose.
• C. Remove all tools and rely on the agent's general capability instead, with the application losing the workflows that previously relied on tools.
• D. Add detailed examples to each tool's description so the agent can match incoming requests to the right tool by example, treating the examples as the team's selection mechanism.

You are building a Claude application that needs to deliver model output to end users as it is generated, instead of waiting for the full response to complete.
The Claude API mechanism you would use is...

• A. The Batch API, which delivers full responses after a delay suitable for non-interactive workloads.
• B. Prompt caching, which speeds up the cost profile of future requests and does not affect the delivery timing of the first response.
• C. Streaming responses, which deliver tokens incrementally as the model generates them so users see output progressively.
• D. Structured JSON output, which delivers responses only after the model has finalized the JSON shape across the entire response.

Your Claude application is hitting context window limits when processing long customer service transcripts.
A junior developer suggests increasing the temperature parameter to fix the issue.
How would you respond?

• A. Explain that temperature controls sampling randomness and is unrelated to context capacity, then address the context issue through summarization or chunking.
• B. Increase the temperature parameter as the junior developer suggested and observe whether the context window issue resolves over the next several runs of the application in production.
• C. Remove the system prompt entirely to make room for longer transcripts in each request, freeing up context window space the system prompt would otherwise consume.
• D. Adjust the temperature parameter together with the max_tokens parameter, treating the combined adjustment as the team's mechanism for managing context window pressure during long-transcript processing.

You are building a Claude application that processes 10,000 customer emails overnight to extract structured data. The work is non-interactive, runs once daily, and has a flexible completion window of several hours.
Which Claude API would you use?

• A. The real-time Messages API with concurrent requests to process the emails as fast as possible during the overnight window.
• B. The streaming responses API to process each email and return partial results to a database as the model generates them.
• C. The Batch API, which is designed for non-interactive workloads with flexible completion windows.
• D. The real-time Messages API, processing the emails one at a time sequentially to ensure consistent ordering of results.