최신Anthropic Claude Certified Architect - Foundations - CCAR-F무료샘플문제

A. When validation fails, make a follow-up request containing the document, extraction, and validation errors so the model can correct the result.

B. Add stricter schema constraints with detailed field descriptions to prevent the model from initially generating invalid values.

C. Retry the extraction up to three times when validation fails, accepting the first result that passes validation.

D. Implement post-processing logic that automatically corrects common errors, such as recalculating totals from line items when sums do not match.

A. Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project's CLAUDE.md.

B. Continue in the current context with more targeted prompts referencing the specific classes by name.

C. Spawn a sub-agent to explore physics independently, then manually synthesize its findings with the rendering knowledge accumulated in the main conversation.

D. Summarize key rendering findings, then spawn a sub-agent for physics exploration with that summary in its initial context.

A. Randomly remove paragraphs.

B. Chunk documents before processing.

C. Increase temperature.

D. Ignore the excess text.

A. Enter plan mode to explore the architecture, evaluate trade-offs, and present options for team approval before implementing.

B. Start direct execution with WebSockets, then refactor if infrastructure issues arise.

C. Use direct execution to implement polling first, then evaluate whether to upgrade to WebSockets later.

D. Use direct execution with a prompt asking Claude to analyze all approaches and implement the one it determines is best.

A. Add a second review pass that rereads the diff using the same prompt and looks for anything the first pass may have missed.

B. Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.

C. Remove all severity-related instructions and allow the model to use its default judgment about which findings to report.

D. Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.

A. Keep one unified tool with a nested operation_details object parameter whose internal structure varies by operation type, documented in the tool description.

B. Keep one unified tool with all parameters marked optional, but add detailed few-shot examples in the system prompt showing correct parameter combinations for each operation type.

C. Keep one unified tool but add JSON Schema if-then-else conditionals to enforce that parameters like amount are required only when the operation type is "refund".

D. Split into three separate tools (issue_refund, cancel_order, request_reshipment), each defining only the parameters required for that specific operation.

A. Index all agent outputs in a shared vector store. When resuming, each agent queries the store using semantic search to retrieve relevant prior findings.

B. Have each agent persist a structured export to a known location. On resume, the coordinator loads the manifest and injects relevant state into agent prompts.

C. Have each agent maintain its own persistent state file and reload it independently at the start of each session.

D. Persist the coordinator's conversation log containing all task delegations and responses, providing this to agents when resuming.

A. Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.

B. Build post-processing keyword filters that suppress findings containing terms such as "force unwrap," "large class," or "deprecated import" before results reach developers.

C. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.

D. Configure the review to analyze only the changed lines in the diff without surrounding file context, reducing the amount of code the model evaluates during each review.

A. Replace the Sonnet-tier model with a Haiku-tier model to obtain a lower per-token price.

B. Issue the 200 Messages API requests concurrently because parallel execution reduces the per- token price.

C. Concatenate all 200 commit messages into one Messages API request because reducing the number of requests always reduces token costs.

D. Submit the 200 requests through the Message Batches API with unique custom_id values and retrieve the results after the batch finishes.

A. Enable extended thinking and instruct the model to reason step by step about every code change before producing its review.

B. Remove all severity-related instructions from the prompt and let the model use its default judgment about what to report.

C. Instruct the model to report all findings with confidence and severity tags, deferring filtering to a downstream step.

D. Add a second review pass that rereads the diff using the same prompt, looking for anything the first pass may have missed.