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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect – Foundations Sample Questions:

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

2. 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.
The system needs to extract candidate information (name, contact details, skills, work experience, education) from uploaded resumes. The extracted data must strictly conform to a predefined JSON schema, as missing required fields or incorrect data types will cause downstream validation failures.
What is the most reliable approach to ensure Claude's output consistently matches the schema?

A) Make two separate API calls-first extracting information as text, then asking Claude to format that text as JSON.
B) Parse Claude's text response with regex patterns to extract JSON objects, using retry logic for malformed responses.
C) Include detailed JSON formatting instructions and a template example in the system prompt, asking Claude to output only valid JSON.
D) Define a tool with an input schema matching your required JSON structure and extract the data from Claude's tool_use response.

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 security audit requires updating your authentication library from v2 to v3. The migration guide documents breaking changes: authenticate() now returns a Promise instead of accepting a callback, the User type has restructured fields, and three deprecated methods were removed. Grep shows the library is imported in 45 files across several modules.
What's the most effective approach?

A) Paste the migration guide's breaking changes into your prompt and use direct execution to update all usages across the 45 files.
B) Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.
C) Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.
D) Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.

4. 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.
An engineer asks your agent to identify untested code paths in a legacy payment processing module spanning
45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate-it' s forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows.
What's the most effective approach to complete this investigation?

A) Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing,"
"trace refund flow dependencies") while the main agent coordinates findings and preserves high-level understanding.
B) Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.
C) Clear context with /clear , then selectively re-read only the most critical files discovered so far, writing key findings to a scratchpad file that persists between context resets.
D) Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.

5. 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 code review assistant needs to analyze pull requests and provide feedback on three aspects: code style compliance, potential security issues, and documentation completeness. Each aspect requires reading files, running analysis tools, and generating a report section. The review process follows the same three-step workflow for every PR.
Which task decomposition pattern is most appropriate for this workflow?

A) Orchestrator-workers-have a central LLM analyze each PR to dynamically determine which checks are needed, then delegate to specialized worker LLMs for each identified subtask.
B) Single comprehensive prompt-include all three instructions in one prompt and let the model handle all three aspects simultaneously.
C) Prompt chaining-break the review into sequential steps where each aspect (style, security, documentation) is analyzed separately, with outputs combined in a final synthesis step.
D) Routing-classify each PR by type (feature, bugfix, refactor) first, then route to different review prompts optimized for that category.

Solutions: