시험대비CCA-F유효한시험최신버전덤프샘플

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Anthropic CCA-F Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Claude Code Configuration & Workflows	20%	- Configuration files and structure 1. Custom commands and workflows 2. CLAUDE.md and rules system - CI/CD and development integration 1. Pipeline automation and review 2. Plan mode vs direct execution
Topic 2: Tool Design & MCP Integration	18%	- Tool definition and best practices 1. Error handling and validation 2. Tool descriptions and selection logic - Model Context Protocol (MCP) 1. MCP server and client setup 2. Tools, resources, and prompts integration
Topic 3: Agentic Architecture & Orchestration	27%	- Agent design patterns 1. Hub-and-spoke multi-agent systems 2. Agent loops and control flow - Anthropic Agent SDK usage 1. Task spawning and tool management 2. Session state and context handling
Topic 4: Prompt Engineering & Structured Output	20%	- Advanced prompting techniques 1. System prompts and role framing 2. Few-shot and chain-of-thought prompting - Structured data generation 1. Output validation and reliability 2. JSON schema enforcement
Topic 5: Context Management & Responsible AI	15%	- Safety and compliance 1. Refusal handling and risk mitigation 2. Constitutional AI principles - Context window optimization 1. Context retention and summarization 2. Token management and truncation strategies

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최신 Claude Certified Architect CCA-F 무료샘플문제 (Q102-Q107):

질문 # 102
When designing an autonomous customer support agent, you must explicitly program the conditions under which it stops attempting to solve the problem and escalates to a human. Which of the following are valid, objective escalation triggers? Choose 2 correct answers.

A. The customer explicitly demands to speak with a human representative.
B. The agent assesses its own confidence level to be below 0.6 out of 1.0.
C. The customer expresses high frustration and uses negative sentiment words.
D. The agent detects a policy gap where no existing rule covers the customer's specific situation.

정답：A,D

설명：
Valid escalation triggers include policy gaps, explicit customer requests for humans, business thresholds being exceeded, and capability limits. Self-reported confidence scores and sentiment analysis are unreliable anti-patterns for escalation decisions.

질문 # 103
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.
After adding an MCP server with specialized code refactoring tools (extract_function, rename_variable, inline_function), You notice the agent still uses basic text manipulation via Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy.
Examining the configuration, you find each MCP tool has a minimal description like
"extract_function: Extracts a function from code."
What's the most effective way to improve adoption of the MCP refactoring tools?

A. Remove the Write tool from the agent's configuration for refactoring sessions so it must use the MCP tools for code modifications.
B. Enhance the MCP tool descriptions to explain when each tool is preferable to text manipulation and clarify expected inputs and outputs.
C. Implement a request classifier that detects refactoring intent and automatically routes those requests to the MCP server before the agent processes them.
D. Accept this as expected behavior since simpler tools like sed are more predictable than specialized refactoring tools.

정답：B

설명：
The minimal descriptions do not distinguish the specialized refactoring tools from general- purpose Write or Bash. Anthropic recommends detailed descriptions that specify what a tool does, when to use it, when not to use it, and its inputs and outputs.

질문 # 104
To extract metadata from a highly variable set of invoices, you use an enum field for document_type. How should you handle unexpected document types to maintain extraction quality?

A. Provide an enum containing only known types and rely on the model to find the closest match.
B. Omit the enum constraint entirely and use an open-ended string field.
C. Implement a PreToolUse hook to drop unrecognized document types.
D. Include an other enum value paired with a detail string field for extensible categorization.

정답：D

설명：
To handle ambiguous or unexpected data during structured extraction, schemas should include an 'other' (or 'unclear') enum value paired with a detail string field. This prevents the model from forcing unexpected data into incorrect predefined categories.

질문 # 105
In a long-running codebase debugging session, the agent makes important intermediate discoveries but starts forgetting earlier constraints as the context window fills. How can you ensure these intermediate discoveries survive context compression boundaries?

A. Declare global variables in the system prompt to hold state.
B. Apply the 'lost in the middle' effect by placing the discoveries in the exact center of the prompt.
C. Persist critical intermediate state to extemal scratchpad files, which survive session boundaries and context resets.
D. Use the - -resume flag to append the new context to the old session.

정답：C

설명：
Scratchpad files are used to persist important intermediate state to external files. Because they are external, they survive context compression (like the /compact command) and allow the agent to re-read its progress when needed.

질문 # 106
You are creating a reusable skill for an automated code review process. You want the skill to analyze the codebase extensively without polluting the main conversation's context window with its exploratory output. Which frontmatter configuration is required?

A. context: fork
B. isolation: true
C. execution_mode: background
D. sandbox: enabled

정답：A

설명：
The 'context: fork' frontmatter option runs the skill in an isolated sub-agent context. This allows the skill to perform verbose exploration without its intermediate steps and noise polluting the main session's context window.

질문 # 107
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