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Anthropic CCAR-F Exam Syllabus Topics:

SectionWeightObjectives
Prompt Engineering & Structured Output20%- Prompt design
  • 1. Output validation
    • 2. Few-shot prompting
      • 3. Prompt engineering techniques
        • 4. Structured output and JSON schemas
          Claude Code Configuration & Workflows20%- Claude Code
          • 1. Agent skills
            • 2. Configuration and project setup
              • 3. Development workflows
                • 4. Code generation and automation
                  Agentic Architecture & Orchestration27%- Agentic architecture patterns
                  • 1. Workflow design
                    • 2. Agent orchestration
                      • 3. Single-agent and multi-agent architectures
                        • 4. Planning and execution strategies
                          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
                                  Context Management & Reliability15%- Context handling
                                  • 1. Cost and performance optimization
                                    • 2. Context window management
                                      • 3. Memory strategies
                                        • 4. Reliability and evaluation

                                          Anthropic Claude Certified Architect – Foundations Sample Questions:

                                          1. 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 system has been running for 3 weeks and human reviewers have corrected 847 extractions. Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or "a splash," the model either invents specific amounts or leaves fields empty-accounting for 23% of all corrections.
                                          How should you use this feedback to improve extraction accuracy?

                                          A) Fine-tune the model on the 847 corrected extractions.
                                          B) Add few-shot examples to your prompt demonstrating correct handling of informal measurements- extracting them verbatim rather than converting or omitting them.
                                          C) Update your JSON schema to add a "measurement_type" enum field (precise/informal).
                                          D) Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.


                                          2. 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 the agent to find all callers of a function before removing it. The function is defined in a core library but is also exposed through wrapper modules that rename the function for domain-specific use (e.
                                          g., calculateTax in the library becomes computeOrderTax in the orders module).
                                          What exploration strategy will most reliably identify all callers?

                                          A) Search for the function name in project documentation to understand intended usage patterns and navigate to documented integration points.
                                          B) Read the library and wrapper modules to identify all exposed names for the function, then Grep for each name across the codebase.
                                          C) Use Grep to search for the function's original name across the codebase.
                                          D) Use Grep to find all files that import from the library or wrapper modules, then read each file to check whether it uses the function.


                                          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.
                                          You need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file.
                                          What is the most appropriate approach?

                                          A) Start with extended thinking mode enabled to ensure thorough reasoning about the validation logic.
                                          B) Use direct execution to make the change.
                                          C) Enter plan mode to analyze how the validation might impact other parts of the reservation flow.
                                          D) Enter plan mode first to create a detailed implementation strategy before making the change.


                                          4. 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) Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.
                                          C) Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."
                                          D) Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.


                                          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.
                                          An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth-having the agent propose specific code changes for each-before deciding which to implement.
                                          What's the most effective way to structure this exploration?

                                          A) Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.
                                          B) Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
                                          C) Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
                                          D) Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.


                                          Solutions:

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

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