Claude Certified Architect Foundations CCAR-F Exam Questions

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Question #1 (Topic: Demo Questions)

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 caching layer for API responses to speed up the/productsendpoint. You have a rough idea—Redis with a 5-minute TTL—but you’re new to production caching and aren’t sure what other considerations a robust implementation requires. What’s the most effective way to start your iterative workflow?

Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.

Use plan mode to analyze the current/productsendpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.

Start with a minimal request: “Add Redis caching to/productswith 5-minute TTL.” Add features and fix issues through follow-up prompts as problems surface during testing.

Write a specification with your known requirements and “TBD” markers for uncertain areas, having Claude propose solutions for each TBD as it implements.

Correct Answer: A

Question #2 (Topic: Demo Questions)

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, andaudit_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?

Use@references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.

Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.

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.

Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.

Correct Answer: A

Question #3 (Topic: Demo Questions)

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, theUsertype 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?

Create a custom slash command encapsulating the migration transformations, then execute it against each file without prior codebase exploration.

Update the dependency version, run the test suite, and use Claude Code to fix each failure as it appears.

Enter plan mode to explore library usage across modules, map affected code paths, then create a migration strategy before implementing.

Paste the migration guide’s breaking changes into your prompt and use direct execution to update all usages across the 45 files.

Correct Answer: C

Question #4 (Topic: Demo Questions)

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 validates outputs against JSON schemas, but you need to implement human review given limited reviewer capacity (they can handle approximately 5% of total extraction volume). What’s the most effective basis for selecting which extractions to route for human review?

Route extractions where the model indicates low confidence or where source documents contain ambiguous or contradictory information.

Route extractions containing specific high-priority entity types (e.g., financial figures, dates) for human review, regardless of extraction confidence.

Route extractions for review only when downstream systems report data quality issues or processing failures

Randomly sample 5% of extractions for review.

Correct Answer: A

Question #5 (Topic: Demo Questions)

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. Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team’s goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?

Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.

Split the workflow into two sequential agent invocations—a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.

Add orchestration-layer code that checks the agent’s outcome after each loop termination—if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.

Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.

Next Question

Correct Answer: C

Explanation:

Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.

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