Anthropic CCAR-F 시험 개요:

Anthropic CCAR-F 시험 요강 주제:

최신 Claude Certified Architect CCAR-F 무료샘플문제

문제 #1

A prompt asks Claude to produce Markdown tables. Which addition MOST increases consistency?

A. Remove formatting instructions.
B. Increase randomness.
C. Specify exact column names.
D. Omit examples.

문제 #2

You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
Your agent has spent 25 minutes exploring a game engine's rendering subsystem -reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
What's the most effective approach?

A. Continue in the current context with more targeted prompts referencing the specific classes by name.
B. Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project's CLAUDE.md.
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.

문제 #3

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
A critical bug is affecting production users. Error logs show exceptions in the OrderProcessing module with a clear stack trace pointing to a specific function. You haven't worked with this module before. What's the most effective approach?

A. Use direct execution to examine the stack trace, read the relevant code, and implement a fix once you identify the root cause.
B. Start with direct execution to gather initial information, then switch to plan mode to design a comprehensive solution before implementing any changes.
C. Enter plan mode to explore the module's architecture and dependencies before attempting any fixes.
D. Use plan mode to analyze the error in context of the module's design, enumerate potential root causes, and prioritize fixes systematically.

문제 #4

In production, final reports frequently contain claims without proper source attribution.
Investigation shows that while the web search and document analysis agents correctly attach citations to their outputs, the synthesis agent loses track of which sources support which conclusions when combining findings. What's the most effective architectural change?

A. Have the coordinator inject source identifier prefixes into text before each handoff, then parse these prefixes at report generation to reconstruct citations.
B. Maintain complete transcripts of all subagent interactions and add a citation-resolution agent to analyze logs and determine attributions before report generation.
C. Add a verification step where the report generator uses semantic similarity matching against original sources to reconstruct which claims came from which documents.
D. Require all subagents to output structured claim-source mappings that the synthesis agent must preserve and merge when combining findings from multiple sources.

문제 #5

Your conversational assistant frequently generates multiple clarifying questions when users make ambiguous requests. When a user asks "Can you help me with the report?", the assistant responds: "I'd be happy to help! Could you tell me: 1) Which report? 2) What kind of help - drafting, reviewing, or formatting? 3) What's your deadline?" User analytics show a 40% conversation abandonment rate after these multi-question responses.
What's the most effective way to reduce friction while appropriately handling ambiguity?

A. Create a lookup table of common request patterns with predefined default interpretations, having the assistant respond with those defaults without stating the assumptions made.
B. Limit the assistant to one clarifying question per turn, using conversation history to accumulate answers over multiple exchanges rather than requesting everything upfront.
C. Add a preprocessing step using a smaller model to classify request ambiguity on a 1-5 scale, routing high-ambiguity requests to a clarification dialog and low-ambiguity requests directly to the assistant.
D. Modify the system prompt to instruct the assistant to make reasonable assumptions from available context, state those assumptions explicitly, and offer to adjust if the interpretation is wrong.

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