Free Anthropic CCDV-F Exam Dumps Questions & Answers

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

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.  Streaming responses, which deliver tokens incrementally as the model generates them so users see output progressively.
• B.  The Batch API, which delivers full responses after a delay suitable for non-interactive workloads.
• C.  Structured JSON output, which delivers responses only after the model has finalized the JSON shape across the entire response.
• D.  Prompt caching, which speeds up the cost profile of future requests and does not affect the delivery timing of the first 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.  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.
• C.  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.
• 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.

A teammate has asked how the Claude SDK handles transient API errors, such as a temporary network issue or a brief rate-limit response. They want to know whether the application code needs to handle every transient error or whether the SDK provides any default behavior.
How would you describe the SDK's default behavior?

• A.  The SDK provides default retry behavior for many transient errors, and the application code can configure or extend that behavior as needed.
• B.  The SDK logs transient errors to a default error stream and continues execution without retrying, leaving the application code responsible for detecting and responding to failed calls.
• C.  The SDK provides default retry behavior for network errors but surfaces rate-limit responses directly to the application code, which must implement its own retry logic for those cases.
• D.  The SDK provides default retry behavior for transient errors up to a fixed number of attempts, and this behavior is not configurable.

You are choosing a Claude model for a high-volume classification task. Each classification is straightforward, latency requirements are tight, and per-request cost matters at scale.
Which model would you choose?

• A.  Multiple Claude models in series, where each request runs through more than one model and the application combines the outputs into a final classification.
• B.  The largest, highest-capability Claude model, to maximize quality on every classification the application produces during normal operation across all requests.
• C.  A mid-tier Claude model selected by default, because mid-tier models balance quality and cost in a way the team can apply across most tasks.
• D.  A smaller, faster Claude model, because the task is straightforward and the workload prioritizes latency and per-request cost at scale.