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Anthropic CCAR-F Exam Syllabus Topics:

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

                                          Question 1

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


                                          Question 2

                                          During initial testing of the automated review pipeline, you notice that reviews of large pull requests containing more than 50 changed files sometimes take over 20 minutes and cost $8-$12 per run because of extensive agentic loops--Claude reads files, runs analysis tools, and iterates many times. Your team needs each invocation to abort after reaching either a fixed iteration count or a fixed dollar amount. Both limits must be enforced by Claude Code itself rather than by the surrounding job runner. Which configuration change directly enforces both per-invocation limits?

                                          A. Add --max-turns 10 --max-budget-usd 2.00 to the claude -p invocation to cap agentic turns and expenditure.
                                          B. Use the --model flag to select a smaller, less expensive model so that every iteration uses fewer tokens and costs less.
                                          C. Set timeout-minutes: 5 on the GitHub Actions step and monitor per-run costs through the Anthropic Console usage dashboard.
                                          D. Set --permission-mode dontAsk to automatically deny tool-permission requests that are not in the explicitly allowed set.


                                          Question 3

                                          Which factor MOST directly affects API cost?

                                          A. CPU clock frequency
                                          B. Internet speed
                                          C. Screen resolution
                                          D. Number of processed tokens


                                          Question 4

                                          Your track_shipment(tracking_id) tool queries an external logistics API that sometimes fails - the API may be temporarily unavailable, the tracking ID may be malformed, or the shipment may not exist. Currently, your tool raises a Python exception when errors occur. Users report the agent gives unhelpful responses like "I'm having trouble with that request" instead of suggesting alternatives such as verifying the tracking number format or checking by order number. How should you handle errors in tool results?

                                          A. Implement retry logic with exponential backoff inside the tool implementation so transient errors are automatically handled and only return a result after all retry attempts are exhausted.
                                          B. Return a generic error response (e.g., {"success": false, "error": "lookup_failed"}) for all failure cases to maintain a consistent schema and avoid exposing internal error details.
                                          C. Return structured error information as normal tool output including error type, recoverability status, and actionable context for the user.
                                          D. Create dedicated error-recovery tools (retry_tracking_lookup, search_by_order_number) that the model can invoke after the primary tracking tool returns a failure indicator.


                                          Question 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.
                                          Your codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today.
                                          The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge. What approach best balances efficiency and accuracy?

                                          A. Start fresh session to ensure the agent works with current codebase state without stale assumptions
                                          B. Resume the session and inform the agent which specific files changed for targeted re-analysis
                                          C. Resume the session without informing the agent about the changed files
                                          D. Resume the session and immediately have the agent re-read all 12 previously analyzed files


                                          Solutions:

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

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