最新的Claude Certified Architect認證 CCAR-F考試題庫

問題1

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:
* Claude must never modify files in the db/migrations/ directory.
* Claude should prefer your custom logging module over console.log .
* 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. 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.

B. 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.

C. 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.

D. Add Edit(./db/migrations/**) to permissions.deny in the project settings, keep the logging preference in CLAUDE.md, and add a PostToolUse hook to run Prettier after TypeScript edits.

正確答案: D

說明：（僅 VCESoft 成員可見）

問題2

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.

正確答案: A

說明：（僅 VCESoft 成員可見）

問題3

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline processes contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30-day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?

A. Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.

B. Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.

C. Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.

D. Redesign the schema so amended fields capture multiple values, each with source location and effective date.

正確答案: D

說明：（僅 VCESoft 成員可見）

問題4

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 wants Claude to follow a detailed code review checklist (8 items covering API changes, test coverage, documentation, security, etc.) when reviewing pull requests. The team also uses Claude extensively for other tasks: writing new features, debugging production issues, and generating documentation. Currently, developers paste the checklist at the start of each review session.
Which approach best addresses this workflow need?

A. Configure plan mode as the default for code review sessions.

B. Create a /review slash command containing the checklist, invoked when starting reviews.

C. Add the checklist to the project's CLAUDE.md file under a "Code Review" section.

D. Create a dedicated review subagent with the checklist embedded in its configuration.

正確答案: B

說明：（僅 VCESoft 成員可見）

問題5

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article-for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?

A. Add prompt instructions to return null for any field where information is not directly stated in the source.

B. Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.

C. Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.

D. Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.

正確答案: A

說明：（僅 VCESoft 成員可見）

問題6

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got '2 to 3'". Retrying these requests without modification produces identical failures.
What's the most effective approach to recover from these validation failures?

A. Send a follow-up request including the validation error, asking the model to correct its output.

B. Implement a secondary pipeline using a larger model tier to reprocess documents that fail validation.

C. Set temperature to 0 to eliminate output variability and ensure consistent formatting.

D. Pre-process source documents to standardize problematic formats before sending them for extraction.

正確答案: A

說明：（僅 VCESoft 成員可見）

問題7

You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
Your agent is handling a billing dispute. After calling get_customer and lookup_order , it identifies that the dispute involves a promotional pricing error requiring manager approval-beyond the agent's authorization level.
How should the workflow handle this mid-process escalation?

A. Call escalate_to_human , passing only the customer's original message.

B. Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.

C. Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human .

D. Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.

正確答案: C

說明：（僅 VCESoft 成員可見）

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