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Anthropic CCA-F Exam Syllabus Topics:

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

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                                          Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions (Q103-Q108):

                                          NEW QUESTION # 103
                                          A customer support agent built with the Claude Agent SDK needs to correctly identify when to resolve a case autonomously versus when to trigger human escalation. Which of the following represents an architectural anti-pattern for triggering escalation?

                                          Answer: D

                                          Explanation:
                                          Using sentiment analysis or the LLM's self-reported confidence score for escalation is a heavily tested anti-pattern. Sentiment does not correlate with task complexity (e.g., an angry customer may just need a simple password reset), and model confidence scores are poorly calibrated for routing decisions.


                                          NEW QUESTION # 104
                                          Your post_content tool requires user confirmation before publishing. The current workflow displays "Ready to post to social media. Confirm?" and analytics show users approve 98% of requests within 2 seconds. Post-mortems reveal incidents where posts went to wrong accounts, were scheduled for wrong times, or contained errors - all confirmed by users without catching the mistakes. How should you redesign the confirmation workflow?

                                          Answer: B

                                          Explanation:
                                          A confirmation step must present the specific action details the user is approving. Showing the full post content, target account, scheduled time, and platform helps users detect errors before execution, making confirmation meaningful rather than a generic approval click.


                                          NEW QUESTION # 105
                                          To reduce false positives and improve developer trust in automated CI/CD code reviews, which prompt engineering techniques should be applied? Choose 2 correct answers.

                                          Answer: A,B

                                          Explanation:
                                          Vague instructions like 'be thorough' lead to over-flagging and alert fatigue. Explicit, measurable criteria and concrete code examples for severity levels establish strict bounds that reduce false positives and build developer trust. Self-reported confidence scores are poorly calibrated and unreliable.


                                          NEW QUESTION # 106
                                          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.
                                          During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

                                          Answer: D

                                          Explanation:
                                          Long exploration sessions fill the context window and degrade recall. A persistent scratchpad externalizes important architectural findings so the agent can re-read them instead of relying solely on increasingly crowded conversation context.


                                          NEW QUESTION # 107
                                          To guarantee that a specific data extraction tool is invoked by Claude during a workflow without the agent attempting conversational workarounds, which configuration must be applied?

                                          Answer: B

                                          Explanation:
                                          Forcing a specific tool with tool_choice: {"type": "tool", "name": "..."} ensures a particular tool runs, which guarantees schema compliance for strict extraction tasks. "any" forces the model to pick a tool, but lets it decide which one. "auto" lets the model decide if it wants to use a tool at all.


                                          NEW QUESTION # 108
                                          ......

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