You are deciding between Claude models for a task. The team has identified three relevant tradeoff dimensions: quality, latency, and cost.
The right model is the one that...

• A. Meets the task's cost target within a defined latency budget, with quality validated against a representative sample of inputs after the model is selected.
• B. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.
• C. Satisfies the task's latency requirement first, then is evaluated against quality and cost thresholds to confirm the selection is acceptable across all three dimensions.
• D. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.

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. Remove all tools and rely on the agent's general capability instead, with the application losing the workflows that previously relied on tools.
• C. 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.
• D. Restructure the tool set by consolidating overlapping tools, removing unused tools, and clarifying tool descriptions so each tool has a distinct purpose.

Your team's Claude agent has accumulated several customizations that bypass the SDK's defaults, including custom history management, retry logic, and error handling. A new team member has proposed reverting all the customizations to maintain the codebase more easily. The tech lead disagrees and says each customization was added for a reason.
How would you advise the team?

• A. Decide on each customization individually based on its original reason and the SDK's current capabilities.
• B. Revert all customizations to the SDK's defaults to standardize the codebase.
• C. Keep all customizations, trusting that the tech lead's original reasoning is still valid.
• D. Migrate the agent off the SDK and rebuild it with a custom loop.

A teammate has asked you to explain the difference between context engineering and prompt engineering.
They have heard the terms used interchangeably and are unsure how each applies to a Claude application that processes long-running multi-step tasks.
How would you describe the distinction?

• A. Prompt engineering and context engineering each address content the team gives Claude, but the team can group them under a single workflow because the practices use overlapping techniques.
• B. Prompt engineering focuses on the model's response, while context engineering focuses on the user's input across many sessions in a long-running multi-step Claude application.
• C. Prompt engineering shapes individual prompts for specific outputs, while context engineering manages how content flows across turns and steps and takes steps to keep relevant state visible.
• D. Prompt engineering is the older term for prompt design, while context engineering is the newer term that has replaced it in modern Claude applications across the industry.

Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?

• A. Increase the model's context window so the agent can hold every tool output at full detail across the entire workflow no matter how many tool calls it accumulates.
• B. Remove tool calling from the workflow entirely so the agent operates as a single text-generation step with no tool outputs accumulating in the context window.
• C. Apply context engineering techniques such as tool output pruning or compaction to keep the active task state visible while reducing the volume of older content.
• D. Restart the agent every five tool calls to prevent any drift, with the agent losing all task state at each restart point during the workflow.