Anthropic CCDV-F Value Pack (Frequently Bought Together)

You are running Claude Code as part of an automated continuous integration pipeline. The pipeline needs Claude Code to execute a set of well-defined tasks without prompting for confirmation, and the output needs to be captured for downstream processing.
How would you configure the pipeline?

• A. Run Claude Code in interactive mode and have a developer manually approve every confirmation prompt while the pipeline executes its tasks.
• B. Replace Claude Code with a different tool that does not require any configuration to operate without confirmation prompts in the pipeline.
• C. Disable Claude Code's confirmation prompts globally across all environments so the pipeline runs without interruption from any prompt.
• D. Run Claude Code in headless mode with the required permissions configured in settings.json and capture its output for downstream processing.

You are implementing a custom tool for your Claude agent. The tool needs to interact with an external pricing service that returns product data.
Which of the following best practices would you apply as you develop this tool?

• A. Define the tool with a clear schema, write a precise description for when to call it, and handle pricing service errors explicitly.
• B. Define the tool with a loose schema and let the model interpret the inputs flexibly on each call the agent makes.
• C. Implement the tool with no error handling and let the agent loop catch failures whenever the pricing service returns an error during operation.
• D. Omit the tool description and let the model infer when to use the tool based on the tool's name and the rest of the prompt context.

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. Prompt caching, which speeds up the cost profile of future requests and does not affect the delivery timing of the first response.
• B. The Batch API, which delivers full responses after a delay suitable for non-interactive workloads.
• 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 application uses the Messages API to handle multi-turn conversations. Each new turn resends the entire conversation history, and your token costs are growing as conversations get longer. You suspect there is a more efficient approach.
How would you address this?

• A. Use prompt caching to reuse the static portions of the conversation context across turns instead of paying for them at the normal input-token rate on every request the application sends.
• B. Summarize each conversation after every turn and replace the full history with the summary on the next request, reducing token count at the cost of fidelity.
• C. Switch to the Batch API for every turn so the per-call cost is reduced, treating the batch as the team's primary cost-control mechanism for multi-turn work.
• D. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.

You are designing a multi-step Claude workflow where some steps must reason without seeing the full prior conversation history. The team wants to keep specific context isolated to specific steps.
The context engineering technique you would use is...

• A. Augmenting the context with all available content at every step so each step has access to the entire prior history of the workflow during its reasoning.
• B. Context isolation through subagents or multi-step agentic workflows that scope each step's context to only what the step needs.
• C. Embedding the full prior history in each step regardless of whether the step needs the prior history for its reasoning.
• D. Using a single global prompt that applies to every step in the workflow no matter what each step is reasoning about during its run.