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 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.
• B. 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.
• C. 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.
• D. 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.

You are deciding between deploying a Claude-powered agent on Anthropic's hosted infrastructure or self- hosting under a "bring your own cloud" model in your own AWS account. The agent processes customer data subject to your enterprise's data residency policies, but the team wants to ship quickly and avoid managing infrastructure.
Which deployment model would you recommend?

• A. Self-hosting under BYOC to satisfy the data residency requirement, while working with the infrastructure team to reduce the operational overhead of managing the deployment.
• B. Self-hosting under BYOC for an initial pilot, then evaluating whether to migrate to Anthropic-hosted infrastructure once the agent's data-handling patterns are better understood.
• C. Deploying on Anthropic-hosted infrastructure while the team drafts a request to update the enterprise data residency policy to accommodate hosted AI deployments.
• D. Deploying on Anthropic-hosted infrastructure to meet the team's shipping timeline, and flagging the data residency requirement for a follow-up compliance review after launch.

Your team's Claude application has been in production for a year, and the team has decided to formalize its testing strategy. Currently, the team writes ad-hoc tests for individual features but has no overall testing approach.
What testing approach would you formalize?

• A. Define a single testing approach that uses end-to-end tests and apply it consistently across all new features as they are added to the codebase.
• B. Adopt a test-driven development practice where unit tests are written before each feature is implemented and must pass before code is merged.
• C. Continue writing ad-hoc tests as features ship and introduce a peer review step to ensure each test adequately covers the feature being released.
• D. Define unit tests for individual functions, integration tests for the Claude integration, and end-to-end tests for critical user flows, applied consistently across the codebase.

You are setting up the configuration management approach for a new Claude Code project. Your team will use CLAUDE.md files and settings.json files to control behavior, and you want to make sure changes are tracked and reviewable.
The configuration management approach would...

• A. Version-control CLAUDE.md and settings.json files alongside the project's source code, with changes reviewed through standard pull request workflows the team applies.
• B. Version-control CLAUDE.md and settings.json files in a separate repository from the project's source code, so configuration evolves independently from the application code over time.
• C. Version-control CLAUDE.md alongside the project's source code and settings.json files in a separate repository from the project's source code.
• D. Duplicate CLAUDE.md and settings.json files in multiple repositories to provide redundancy, on the grounds that a single source of truth is risky for project configuration.

The product team has asked you to choose a Claude model for a new feature. The team has provided functional requirements but has not specified performance, cost, or quality targets. The team's product manager says, "Use whatever model gives us the best results." How would you respond?

• A. Run every Claude model on a representative sample and pick whichever scores best on a generic benchmark.
• B. Choose a mid-tier model and ship the feature, because mid-tier models work for most use cases without specified targets.
• C. Choose the largest, highest-capability Claude model, on the grounds that "best results" is most likely to mean highest quality.
• D. Ask the product team to specify quality, latency, and cost targets, then select the model whose tradeoffs best fit those targets.