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Your team is preparing a new Claude application for production, and the product team has asked for a cost projection. The team needs to estimate the cost based on expected request volume, average input length, and average output length. How would you build the projection?

• A. Build a cost model that combines expected request volume, average input tokens, average output tokens, the chosen model's pricing, and any caching benefits.
• B. Build a cost model that combines expected request volume and average input tokens, treating output tokens as a small enough share of cost to leave out of the projection.
• C. Build a cost model based on expected request volume and the chosen model's pricing, treating average input and output token counts as variables to be estimated post-launch.
• D. Build a cost model that uses the average per-request cost from a similar Claude application the team built last year, scaling that figure by expected request volume.

The team is debating whether to build a new capability as a custom tool or to use an existing built-in tool that nearly covers the use case but lacks one specific feature.
How would you decide?

• A. Identify whether the team has previously built similar custom tools and use those precedents to determine which approach is more consistent with the existing codebase.
• B. Evaluate whether the missing feature can be addressed by extending the built-in tool or warrants a custom tool, then choose accordingly.
• C. Examine the custom tool option by scoping the implementation effort and determining whether the additional flexibility justifies the development and maintenance cost.
• D. Review the built-in tool's documentation and assess whether its existing capabilities are sufficient to cover the use case without any modification or extension.

You maintain a Claude application that uses Claude Sonnet 4.5 across several production workflows.
Anthropic released Claude Sonnet 4.7, which your evaluation suite shows performing 8% better on your highest-volume task. However, this version produces different output formatting on two of your structured- extraction prompts that downstream consumers parse with regex-based code.
To roll out the upgrade, you would...

• A. Keep the older model in production to preserve the fragile parsing layer that may risk breaking with any model change.
• B. Rewrite the downstream parsing code to accept a wider range of output formats to ensure the application is resilient to future model upgrades without requiring prompt changes.
• C. Adjust the prompts to constrain output format, re-run evaluations against the expected parsing-layer schema, then roll the model out with a feature flag and the option to revert per workflow.
• D. Switch the model identifier in the application's configuration to the new model, then monitor production for parsing failures to be fixed as failures surface.

Your Claude application is deployed to development, staging, and production environments. Each environment uses a different model version, different prompt versions, and different plugin dependencies, but the configuration is currently scattered across environment variables, hardcoded values, and undocumented setup scripts.
How would you manage the configuration?

• A. Standardize all environments to use the same configuration values to eliminate the differences between development, staging, and production.
• B. Move all configuration into hardcoded application code to reduce reliance on external configuration sources that are difficult to track over time.
• C. Consolidate the configuration into a version-controlled system documenting model version pinning, prompt versioning, and plugin dependencies for each environment.
• D. Use the latest available model version everywhere and stop pinning model versions, on the grounds that pinning adds maintenance overhead the team should aim to reduce.

Your agent makes 10 to 15 tool calls per task, and you have noticed it sometimes loses track of earlier results by the time it reaches later steps. The agent's context window is large enough to hold all the messages, but the relevant information appears to get buried as the conversation grows.
How would you address this?

• A. Increase the context window further so all tool outputs from every prior step remain in full detail throughout the task.
• B. Switch to a different agentic framework that advertises automatic context-window management as a built-in feature.
• C. Apply a context-management pattern that summarizes or prunes older tool outputs while preserving the active task state.
• D. Reduce the number of tool calls per task by combining several existing tools into larger, multi-purpose tools.