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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

A. Switch to a higher-capacity model tier to provide more context window space for accumulated exploration data.
B. Create summaries of all source files before exploration begins, loading only these compressed representations into context.
C. Implement automatic context clearing every 15 minutes to ensure the agent starts with fresh, uncontaminated context.
D. Have the agent maintain a scratchpad file that records key findings, referencing it for subsequent questions.

Question 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 team frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing /migrate-component. The workflow should stay in sync as the team iterates on it. Where should you place the skill file?

A. As a detailed instruction block in the project's root CLAUDE.md file
B. In the project's.claude/settings.json using a skillOverrides entry to register and define the workflow
C. In ~/.claude/skills/migrate-component/SKILL.md on each developer's machine
D. In .claude/skills/migrate-component/SKILL.md at the project root, committed to version control

Question 3

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. Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.
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/**/*"]), loading rules only when editing matching files.
D. Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g, "## Terraform Conventions"), improving organization and readability.

Question 4

The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?

A. Split the generic tool into purpose-specific tools - extract_data_points, summarize_content, verify_claim_against_source - each with defined input/output contracts
B. Have the coordinator pre-classify each analysis request before passing instructions to the document analysis agent
C. Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
D. Keep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes

Question 5

After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

A. Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.
B. Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
C. Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.
D. Pass the full accumulated context from all prior agents.

Solutions: