Anthropic Claude Certified Architect – Foundations : CCAR-F exam dump

  • Exam Code: CCAR-F
  • Exam Name: Claude Certified Architect – Foundations
  • Updated: Jul 28, 2026
  • Q&As: 62 Questions and Answers

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Anthropic CCAR-F Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Context Management & Reliability15%- Context handling
  • 1. Memory strategies
    • 2. Cost and performance optimization
      • 3. Context window management
        • 4. Reliability and evaluation
          Topic 2: Agentic Architecture & Orchestration27%- Agentic architecture patterns
          • 1. Single-agent and multi-agent architectures
            • 2. Agent orchestration
              • 3. Workflow design
                • 4. Planning and execution strategies
                  Topic 3: Tool Design & MCP Integration18%- Tool integration
                  • 1. Model Context Protocol (MCP)
                    • 2. Tool interface design
                      • 3. Resource and server integration
                        • 4. Tool selection and safety
                          Topic 4: Prompt Engineering & Structured Output20%- Prompt design
                          • 1. Output validation
                            • 2. Few-shot prompting
                              • 3. Prompt engineering techniques
                                • 4. Structured output and JSON schemas
                                  Topic 5: Claude Code Configuration & Workflows20%- Claude Code
                                  • 1. Development workflows
                                    • 2. Configuration and project setup
                                      • 3. Agent skills
                                        • 4. Code generation and automation

                                          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.
                                          You're implementing a caching layer for API responses to speed up the /products endpoint. 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?

                                          A) Use plan mode to analyze the current /products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.
                                          B) Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
                                          C) Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
                                          D) Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.


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


                                          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 schema includes a skills: string[] field. Production monitoring reveals three consistency issues: (1) compound phrases like "Python and SQL" are sometimes kept as one entry, sometimes split; (2) implied but unstated skills occasionally appear in extractions; (3) similar documents produce wildly different array lengths (5-10 vs 40+ entries). Your prompt currently says "Extract all skills mentioned." What's the most effective improvement?

                                          A) Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.
                                          B) Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.
                                          C) Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."
                                          D) Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.


                                          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 add comprehensive tests to a legacy codebase with 200 files and minimal existing test coverage. The engineer hasn't specified which modules to prioritize.
                                          How should the agent decompose this open-ended task?

                                          A) Use Glob and Grep to map codebase structure, identify heavily-coupled modules, create a prioritized plan for high-impact areas, and revise as dependencies are discovered.
                                          B) Systematically read all 200 files to create a complete function inventory before writing any tests, ensuring the testing plan accounts for every function before beginning.
                                          C) Create a fixed testing schedule upfront based on directory structure, allocating equal effort to each top- level directory regardless of code complexity or business importance.
                                          D) Start writing tests for the first module alphabetically, using test failures and imports to discover related files organically.


                                          5. 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) Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
                                          C) Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human .
                                          D) Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.


                                          Solutions:

                                          Question # 1
                                          Answer: B
                                          Question # 2
                                          Answer: A
                                          Question # 3
                                          Answer: D
                                          Question # 4
                                          Answer: A
                                          Question # 5
                                          Answer: C

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