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Anthropic CCA-F Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Tool Design & MCP Integration18%- Tool definition and best practices
  • 1. Tool descriptions and selection logic
    • 2. Error handling and validation
      - Model Context Protocol (MCP)
      • 1. MCP server and client setup
        • 2. Tools, resources, and prompts integration
          Topic 2: Context Management & Responsible AI15%- Safety and compliance
          • 1. Refusal handling and risk mitigation
            • 2. Constitutional AI principles
              - Context window optimization
              • 1. Token management and truncation strategies
                • 2. Context retention and summarization
                  Topic 3: Prompt Engineering & Structured Output20%- Structured data generation
                  • 1. Output validation and reliability
                    • 2. JSON schema enforcement
                      - Advanced prompting techniques
                      • 1. System prompts and role framing
                        • 2. Few-shot and chain-of-thought prompting
                          Topic 4: Agentic Architecture & Orchestration27%- Anthropic Agent SDK usage
                          • 1. Task spawning and tool management
                            • 2. Session state and context handling
                              - Agent design patterns
                              • 1. Agent loops and control flow
                                • 2. Hub-and-spoke multi-agent systems
                                  Topic 5: Claude Code Configuration & Workflows20%- CI/CD and development integration
                                  • 1. Pipeline automation and review
                                    • 2. Plan mode vs direct execution
                                      - Configuration files and structure
                                      • 1. Custom commands and workflows
                                        • 2. CLAUDE.md and rules system

                                          Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:

                                          1. When the agent calls lookup_order and receives order details showing the item was purchased
                                          45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?

                                          A) The agent executes the remaining steps in a tool sequence planned at the start of the request.
                                          B) The order details are added to the conversation and the model reasons about which action to take.
                                          C) The orchestration layer automatically routes to the next tool based on the order's status field.
                                          D) The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.


                                          2. When implementing your lookup_order MCP tool, the backend sometimes returns errors (e.g.,
                                          "Order not found" or temporary database failures). What is the correct pattern for communicating these errors back to the agent?

                                          A) Throw an exception from the tool handler so the agent framework can catch and log it
                                          B) Return the error message in the tool result content with the isError flag set to true
                                          C) Log the error server-side and return an empty result to avoid confusing the model
                                          D) Return a success response with a "status" field indicating the error type


                                          3. You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
                                          When analyzing complex legal cases that cite multiple precedents, the document analysis subagent processes each sequentially. A landmark case citing 12 precedents takes over 3 minutes to analyze completely. What's the most effective way to reduce this latency while preserving the coordinator's ability to monitor and debug the system?

                                          A) Have the coordinator spawn parallel document analysis subagents, each handling a subset of precedents, then aggregate results before synthesis
                                          B) Create a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity
                                          C) Enable the document analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations
                                          D) Implement a message queue where precedent analysis tasks are processed asynchronously by a pool of worker agents


                                          4. Your MCP server includes archive_file(file_id) and delete_file(file_id) tools. Production logs show the agent calls delete_file when users ask to "remove old backups," but company policy requires archiving backup files. Both tools currently have minimal descriptions: "Archives a file" and
                                          "Deletes a file." Which change most directly improves tool selection for this scenario?

                                          A) Add a confirmation step that requires users to type "CONFIRM DELETE" before delete_file executes.
                                          B) Implement server-side validation that rejects delete_file calls for files tagged as backups, returning an error message suggesting archive_file.
                                          C) Expand tool descriptions to clarify use cases, adding guidance like "Do not use for backup files" to delete_file.
                                          D) Add few-shot examples to the system prompt demonstrating that requests involving "backup" or
                                          "old" should use archive_file.


                                          5. You're implementing the escalation logic for when the agent should call escalate_to_human. Your team proposes four different approaches for triggering escalation. Which approach will most reliably identify cases that genuinely require human intervention?

                                          A) Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.
                                          B) Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
                                          C) Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and trigger escalation when the frustration score exceeds a configured threshold.
                                          D) Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.


                                          Solutions:

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

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