Anthropic CCDV-F : Claude Certified Developer-Foundations

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. Truncate every conversation to the last two messages so that token usage stays low and costs remain predictable across the application's normal operation.
• 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. 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.

Your team is preparing to roll out a configuration change that updates several prompt versions across a Claude application used by multiple downstream systems. The change has already been tested in staging, but the team has not assessed how the prompt change will affect each downstream system that depends on the application's output.
What would you do before rolling out the change?

• A. Limit the rollout to systems that were explicitly included in staging testing, and defer all other downstream systems until a later release cycle.
• B. Document the prompt version changes in the application changelog and proceed with the rollout, treating the staging test results as sufficient evidence of impact across all downstream systems.
• C. Notify downstream system owners that a change is coming and schedule the rollout for the following week, without conducting a formal impact assessment.
• D. Assess the configuration impact on each downstream system before rolling out, and coordinate with downstream system owners as needed.

You are designing an agent that processes vendor invoices. The work involves a small number of well- understood steps, but occasionally an invoice arrives in an unexpected format that requires the system to decide between rerouting, requesting clarification, or flagging for human review.
The most appropriate architecture for this system is...

• A. A fully autonomous agent that handles every invoice from start to finish across all formats.
• B. A workflow for the standard path with an agent invoked at the decision point for unexpected formats.
• C. A single large prompt that processes every incoming invoice, both standard and unexpected, in one model call.
• D. A manager agent that delegates each step of standard invoice processing to a dedicated subagent, with a separate subagent handling each unexpected format.

You are designing a Claude application that maintains user sessions across multi-turn conversations. The product team has asked how the application will handle session lifecycle: when sessions should expire, how state is reset, and how the application avoids carrying stale context into new conversations.
How would you design session lifecycle?

• A. Define explicit session expiration rules but rely on users to start new conversations when they want fresh context, with no automatic reset triggers in the application.
• B. Define a single short session timeout that applies across all conversations and treat the timeout as the application's complete session lifecycle mechanism.
• C. Define explicit session expiration rules, state reset triggers, and rules for starting fresh sessions so stale context does not leak into new conversations.
• D. Define state reset triggers tied to specific application events but apply them across all sessions globally, with no per-session expiration rules.

You are building an agent that needs to call several internal APIs and a database in a structured, repeatable way. Your team has decided to use the Claude Agent SDK rather than build a custom loop. You are setting up the agent's tool definitions and execution loop.
How would you set up the tools and execution loop?

• A. Use the SDK's tool interface and write the loop and history layer in the team's own code.
• B. Use the SDK's tool interface and let the SDK handle the loop, dispatch, and history.
• C. Use the SDK's tool interface and loop, with conversation history stored in a separate team database.
• D. Call the Messages API directly and let the model format its tool calls in plain text.