최신Anthropic Claude Certified Developer-Foundations - CCDV-F무료샘플문제

A. Identify the failure mode as format drift, examine how the conversation context evolves over turns, and address the drift through context management or output validation.

B. Truncate every response to the first few characters, validate that the truncated output matches the expected JSON structure, and log any mismatches for review.

C. Switch to a smaller Claude model and re-test the application to determine whether the drift persists across conversation turns.

D. Restart the application after every turn and monitor whether the format remains consistent across subsequent interactions.

A. Increase the context window further so all tool outputs from every prior step remain in full detail throughout the task.

B. Apply a context-management pattern that summarizes or prunes older tool outputs while preserving the active task state.

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. Run Claude Code in headless mode with the required permissions configured in settings.json and capture its output for downstream processing.

B. Run Claude Code in interactive mode and have a developer manually approve every confirmation prompt while the pipeline executes its tasks.

C. Replace Claude Code with a different tool that does not require any configuration to operate without confirmation prompts in the pipeline.

D. Disable Claude Code's confirmation prompts globally across all environments so the pipeline runs without interruption from any prompt.

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 loop, with conversation history stored in a separate team database.

C. Call the Messages API directly and let the model format its tool calls in plain text.

D. Use the SDK's tool interface and let the SDK handle the loop, dispatch, and history.

A. Assess the cost and latency implications of both approaches against the application's performance requirements before recommending which processing strategy to adopt.

B. Examine the use cases where cross-section reasoning failures occur and assess whether whole- document processing meets the application's accuracy requirements across a representative sample of contracts.

C. Review the citation accuracy results from chunked processing and determine whether the loss of cross- section reasoning produces summaries that still meet the application's quality bar.

D. Identify which property matters more for the application's actual use case and let that decision drive the chunking approach, then validate the choice against representative contracts.

A. Explain that LLMs are non-deterministic by default due to sampling, and describe how the application handles this through validation, retries, or temperature adjustment.

B. 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.

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. Tell the stakeholder the variation comes from network latency and that switching to a faster network connection will produce more consistent results across runs.

A. Meets the task's cost target within a defined latency budget, with quality validated against a representative sample of inputs after the model is selected.

B. Fits the task's quality, latency, and cost requirements together, recognizing that improving one dimension typically affects the others.

C. Meets the task's quality requirements at an acceptable latency, with cost reviewed separately once the quality and latency bar has been established.

D. Satisfies the task's latency requirement first, then is evaluated against quality and cost thresholds to confirm the selection is acceptable across all three dimensions.

A. Rewrite the downstream parsing code to accept a wider range of output formats to ensure the application is resilient to future model upgrades without requiring prompt changes.

B. Adjust the prompts to constrain output format, re-run evaluations against the expected parsing-layer schema, then roll the model out with a feature flag and the option to revert per workflow.

C. Keep the older model in production to preserve the fragile parsing layer that may risk breaking with any model change.

D. Switch the model identifier in the application's configuration to the new model, then monitor production for parsing failures to be fixed as failures surface.

A. Explain that LLMs generate output token by token, and variation in how prompt content appears in output is a property of generation, not a bug in request construction.

B. Confirm that the variation is a bug in the application's request construction and start investigating which part of the application is producing the inconsistent prompt text.

C. Tell the teammate that the variation depends on which Claude model serves the request, and recommend pinning the application to a single model version to make the output behavior consistent.

D. Tell the teammate that the variation is caused by the application sending two different prompts on different runs and propose a code change that pins the system prompt to a single version.

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.