Instantly download CCDV-F updated real questions

You are explaining to a stakeholder why running the same Claude prompt twice can produce slightly different results. The stakeholder is concerned this means the application is broken.
How would you address the stakeholder's concern?

• A. Tell the stakeholder the variation is caused by Claude being updated continuously by Anthropic, and that switching to a fixed model snapshot will eliminate the variation entirely.
• B. Tell the stakeholder the variation comes from network latency and that switching to a faster network connection will produce more consistent results across runs.
• C. Tell the stakeholder the variation is a bug that the team will fix in the next release of the application, then create a work ticket to fix the bug.
• D. Explain that LLMs are non-deterministic by default due to sampling, and describe how the application handles this through validation, retries, or temperature adjustment.

You are reviewing an architectural diagram for a Claude-powered travel-booking system. The diagram shows a top-level component that interprets user requests and three subordinate components that handle flights, hotels, and ground transportation. The top-level component is responsible for routing each request, sequencing the subordinate components, and reconciling their outputs into a final itinerary. The diagram also shows that each subordinate component has its own tool list and own short conversation history that is not shared with the others.
Which architectural pattern does this diagram most closely describe?

• A. A retrieval-augmented pattern, where the top-level component retrieves relevant context from the subordinate components' indexed data stores before generating each response.
• B. A manager and supervisor pattern with isolated context per subagent, where the top-level agent coordinates specialized subagents that each maintain their own conversation history and tool list.
• C. A manager and supervisor pattern with shared context, where the top-level agent and the subagents all share a single conversation history that grows as the request flows through the system.
• D. A pipeline pattern, where each component processes the user request in sequence and passes the full conversation history along with the request to the next component in the pipeline.

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

A new Claude model release includes performance improvements for several reasoning tasks but has changed the format of its responses to system prompts that use multi-section instructions. Your application uses multi- section system prompts heavily. Initial evaluation on the application's actual workload shows the new model performs 8 percent better on reasoning tasks but produces malformed output on roughly 3 percent of requests because of the format change. The team is debating whether to upgrade.
How would you decide?

• A. Upgrade immediately, because the 8 percent reasoning improvement outweighs the 3 percent malformed output rate across the application's typical request distribution.
• B. Stay on the previous model permanently to avoid the malformed output rate and any future format changes that subsequent model releases might introduce in the application.
• C. Adapt the application's system prompt to the new model's format expectations and re-evaluate, then upgrade only if the adapted prompt eliminates the malformed output while preserving the reasoning improvements.
• D. Upgrade and add a downstream validation step that catches the 3 percent malformed output before it reaches users, treating the validation step as the team's mitigation for the format change.

A teammate is debugging a Claude application whose system prompt has grown to several hundred lines and now contains overlapping, contradictory, and obsolete instructions.
How would you advise the teammate?

• A. Tighten only the contradictory rules first, treating the overlap and obsolete content as lower-priority work the team can address later.
• B. Audit the prompt for overlap, contradiction, and obsolete content, then refactor so each instruction is clear, current, and non-redundant.
• C. Add more explicit instructions so the most recent rules dominate the model's interpretation of the prompt during each request.
• D. Split the prompt across multiple system prompts so the model sees a smaller portion at any given time.