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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect – Foundations Sample Questions:

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.
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.

2. 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.
A customer returns 4 hours after their initial session about the same billing dispute. The previous 32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution: 24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information.
What approach most reliably handles returning customers?

A) Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.
B) Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.
C) Resume with full history and configure the agent to automatically re-call all previously used tools at session start to ensure data freshness.
D) Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.

3. 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.
During a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order , but when attempting to call process_refund , the tool returns a timeout error.
The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure.
What approach best balances first-contact resolution with appropriate error handling?

A) Escalate immediately to a human agent since the refund action cannot be completed.
B) Implement automatic retries with exponential backoff for process_refund , keeping the conversation open until the refund is successfully processed.
C) Explain the billing, confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later.
D) Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically.

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.
You're implementing a new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py , error_handlers.py , and audit_logger.py . This is a one-off integration task-these patterns are well-documented in your team wiki and don't need additional project-level documentation.
What's the most effective approach?

A) Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.
B) Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow.
C) Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.
D) Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.

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.
After integrating a local MCP server providing code analysis tools ( analyze_dependencies , find_dead_code , calculate_complexity ), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
* MCP analyze_dependencies - "Analyzes dependency graph"
* Built-in Grep - "Search file contents for a pattern using regular expressions. Returns matching lines with line numbers and surrounding context." What's the most effective approach to improve the agent's selection of MCP tools?

A) Expand MCP tool descriptions to detail capabilities and outputs-e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."
B) Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.
C) Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.
D) Split analyze_dependencies into granular tools ( list_imports , resolve_transitive_deps , detect_circular_deps ) so each has a focused purpose less likely to overlap with Grep.

Solutions: