CCAR-F높은통과율시험대비덤프공부 - CCAR-F자격증참고서

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Anthropic CCAR-F Exam Syllabus Topics:

Section	Weight	Objectives
Claude Code Configuration & Workflows	20%	- Claude Code 1. Configuration and project setup 2. Agent skills 3. Code generation and automation 4. Development workflows
Prompt Engineering & Structured Output	20%	- Prompt design 1. Few-shot prompting 2. Output validation 3. Prompt engineering techniques 4. Structured output and JSON schemas
Context Management & Reliability	15%	- Context handling 1. Cost and performance optimization 2. Context window management 3. Reliability and evaluation 4. Memory strategies
Agentic Architecture & Orchestration	27%	- Agentic architecture patterns 1. Planning and execution strategies 2. Workflow design 3. Single-agent and multi-agent architectures 4. Agent orchestration
Tool Design & MCP Integration	18%	- Tool integration 1. Tool selection and safety 2. Resource and server integration 3. Model Context Protocol (MCP) 4. Tool interface design

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Anthropic CCAR-F자격증참고서, CCAR-F인증시험 공부자료

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최신 Claude Certified Architect CCAR-F 무료샘플문제 (Q140-Q145):

질문 # 140
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.
Your infrastructure-as-code repository includes Terraform modules ( /terraform/ ), Kubernetes manifests (
/kubernetes/ ), and CI/CD pipeline scripts ( /pipelines/ ). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?

A. Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*.tf"] ), loading rules only when editing matching files.
B. Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g., "## Terraform Conventions"), improving organization and readability.
C. Split content into subdirectory CLAUDE.md files ( /terraform/CLAUDE.md , /kubernetes/CLAUDE.
md ), so Claude loads directory-specific guidance.
D. Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.

정답：A

설명：
Path-scoped rules directly satisfy the requirement that instructions load only when Claude works with matching files. Each infrastructure domain can have a separate Markdown file under .claude/rules/ , with YAML paths frontmatter targeting Terraform files, Kubernetes manifests, or pipeline configuration.
Anthropic documents that .claude/rules/ keeps project guidance modular and supports conditional loading based on glob patterns. Rules with a paths field apply only when Claude works with matching files, reducing irrelevant context and token consumption. Rules without path frontmatter load unconditionally. ( https://code.
claude.com/docs/en/memory )
Option B improves readability but leaves all 500-plus lines in every session. Option C can provide directory- specific CLAUDE.md guidance, but path-scoped rules are more precise when conventions depend on extensions or patterns spanning multiple directories. Option D still requires manual imports and does not provide the documented conditional rule-loading mechanism.
A suitable design would use rules such as terraform.md scoped to terraform/**/*.tf , kubernetes.md scoped to kubernetes/**/*.{yaml,yml} , and pipelines.md scoped to the pipeline directory. Broad project-wide commands and repository etiquette can remain in the root CLAUDE.md.
Official references/topics: .claude/rules/ ; Path-Specific Rules; YAML Frontmatter; Context-Efficient Instruction Loading.

질문 # 141
You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline.
The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
Your automated reviewer uses a single prompt covering security issues, API design, and business-logic correctness. Your evaluation suite shows strong recall for API-design findings at 82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add few-shot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recall drops to 68%.
How should you address this trade-off to improve detection across both categories?

A. Replace the few-shot examples with a detailed checklist of specific logic edge cases to verify, such as division by zero in score calculations and boundary conditions in grading thresholds.
B. Provide the full repository as context instead of only the changed files and surrounding code, giving the model deeper visibility into business-logic patterns.
C. Split the review into separate focused prompts-one for security and API design and another for business logic-each with dedicated examples, and then consolidate the findings before posting.
D. Upgrade to a more capable model tier because its stronger reasoning will handle both concern types in one prompt and eliminate the recall trade-off.

정답：C

설명：
Option A separates competing objectives so each review call can devote its attention and examples to a coherent concern. Anthropic's Building Effective AI Agents describes sectioning as a parallelization pattern in which independent aspects are handled by separate model calls and then aggregated. It specifically notes that models generally perform better on complex tasks when each consideration receives focused attention.
The evaluation results demonstrate prompt interference: optimizing business-logic detection reduces API- design recall.
A security and API-design reviewer can use examples, terminology, and evidence criteria appropriate to interfaces and vulnerabilities. A business-logic reviewer can focus on state transitions, arithmetic boundaries, invariants, and domain-specific edge cases. Their structured findings can then be deduplicated and ranked before posting. Option B may improve only the weak category and can overfit to known quiz-scoring failures.
Option C assumes that model capacity alone removes prompt interference without evaluation evidence.
Option D adds substantial unrelated context and does not isolate the competing review objectives. The split prompts should be evaluated independently and together so consolidation does not reintroduce omissions or duplicate findings.

질문 # 142
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.
Your team has three requirements for Claude Code's behavior in your project
1. Claude must never modify files in the db/migrations/ directory
2. Claude should prefer your custom logging module over console.log
3. All TypeScript files must be auto-formatted with Prettier after
every edit
All three are currently written as instructions in your project's CLAUDE.md. During a complex refactoring session, a developer discovers that Claude edited a migration file, violating requirement #1. How should you restructure these requirements across Claude Code's configuration mechanisms?

A. Add Edit(./db/migrations/**) to permissions.deny in the project settings, keep the logging preference in CLAUDE.md, and add a PostToolUse hook on the Edit tool that runs Prettier on changed TypeScript files.
B. Rewrite all three requirements in CLAUDE.md using stronger directive language and add few- shot examples that demonstrate Claude refusing to edit migration files and running Prettier after edits.
C. Configure hooks for all three: a PreToolUse hook script that blocks Edit calls targeting db/migrations/,a PreToolUse hook script that adds logging convention context before edits, and a PostToolUse hook that runs Prettier after TypeScript edits.
D. Move all three requirements into .claude/rules/ as path-scoped rules: one targeting db/migrations/** that forbids editing those files, and others targeting **/*.ts for the logging convention and formatting instruction.

정답：A

설명：
Hard restrictions belong in permission controls, coding preferences belong in CLAUDE.md, and deterministic formatting belongs in an automated hook. Instructions alone cannot reliably prevent prohibited file edits.

질문 # 143
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.
Your team is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing.
Which configuration approach correctly applies MCP server scopes?

A. Add both servers to the project-level .mcp.json file.
B. Add both servers to your local ~/.claude.json .
C. Add the venue server to .mcp.json and the playlist server to ~/.claude.json .
D. Add the venue server to ~/.claude.json and the playlist server to .mcp.json .

정답：C

설명：
The venue server must be available to every project contributor, so it belongs at project scope. Claude Code stores project-scoped MCP configurations in the repository-root .mcp.json file, which can be committed to version control and shared with the team.
The experimental playlist server should remain private. Local-scoped MCP servers are stored under the current project's entry in ~/.claude.json , while user-scoped servers are also stored in ~/.claude.json but load across all projects. Because this server is described as a personal experiment, either private scope is conceptually appropriate; among the listed choices, option B correctly separates shared project configuration from private personal configuration. ( https://code.claude.com/docs/en/mcp ) Option A prevents the team from receiving the venue configuration. Option C reverses the intended visibility.
Option D exposes the experimental server configuration to other contributors and may commit machine- specific commands or credentials.
The preferred commands would specify --scope project for the shared venue server and --scope local for an experiment limited to the current repository. Project-scoped servers also require user approval before Claude Code activates configurations received through .mcp.json .
Official references/topics: MCP Installation Scopes; Project Scope; Local Scope; .mcp.json ; ~/.claude.json .

질문 # 144
In addition to your CI pipeline, your organization has enabled Claude's managed Code Review through the Claude GitHub App on this repository, and reviews run automatically on every pull request. Reviews average 18 findings per pull request. Developer feedback reveals three categories of unwanted noise: (1) style and formatting issues already enforced by your CI linter, (2) findings on automatically generated template code under src/gen/*, and (3) rendering-helper patterns that are intentional project conventions but are flagged because they resemble common anti-patterns. Only approximately four findings per pull request are genuine logic bugs. What is the most effective way to reduce this noise while preserving the detection of real issues?

A. Create a REVIEW.md file at the repository root containing skip rules for CI-enforced checks and generated files, together with a verification requirement that rendering-related findings cite a specific line demonstrating incorrect behavior.
B. Add custom review instructions to a GitHub Actions workflow file, using the action's prompt parameter to suppress duplicate lint findings, ignore generated template code, and impose stricter evidence requirements on rendering-related issues.
C. Add detailed explanations to the project's CLAUDE.md describing intentional patterns, stating that CI handles linting, and identifying src/gen/ as automatically generated code.
D. Configure separate GitHub Actions workflow files for each code area: one for generated code with findings suppressed, one for rendering code with custom instructions, and one general workflow for everything else.

정답：A

설명：
Option A uses the dedicated configuration mechanism for Anthropic's managed Code Review service. The official Code Review documentation states that a root-level REVIEW.md is injected into every review agent as the highest-priority instruction block. It can redefine severity, suppress categories already enforced by CI, skip generated paths, and require concrete evidence before particular findings are reported.
The proposed rules map directly to the observed noise. Skipping lint and formatting findings eliminates duplication with deterministic CI checks. Excluding src/gen/* prevents comments on machine-generated code. Requiring a specific source line demonstrating incorrect behavior raises the verification threshold for rendering-helper findings without suppressing genuine defects.

질문 # 145
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