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Anthropic CCA-F Exam Overview:

Certification Vendor:Anthropic
Exam Name:Anthropic Claude Certified Architect Foundations (CCA-F)
Exam Number:CCA-F
Real Exam Qty:60
Certificate Validity Period:6 months
Exam Price:$99 USD
Passing Score:720/1000
Exam Format:Multiple-choice, Multiple-select, Scenario-based questions
Exam Duration:120 minutes
Available Languages:English
Recommended Training:Claude Developer Documentation
Anthropic Official Learning Resources
Exam Registration:Official Registration
Sample Questions:Anthropic CCA-F Sample Questions
Exam Way:Online, remotely proctored via webcam
Pre Condition:Recommended: practical experience building with Claude API or tools; initially available only to employees of Anthropic partner companies
Official Syllabus URL:https://www.anthropic.com/certification/cca-f

Anthropic CCA-F Exam Syllabus Topics:

SectionWeightObjectives
Prompt Engineering & Structured Output20%- Structured data generation
  • 1. Output validation and reliability
    • 2. JSON schema enforcement
      - Advanced prompting techniques
      • 1. Few-shot and chain-of-thought prompting
        • 2. System prompts and role framing
          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. Pipeline automation and review
                • 2. Plan mode vs direct execution
                  Agentic Architecture & Orchestration27%- Agent design patterns
                  • 1. Hub-and-spoke multi-agent systems
                    • 2. Agent loops and control flow
                      - Anthropic Agent SDK usage
                      • 1. Task spawning and tool management
                        • 2. Session state and context handling
                          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. Tools, resources, and prompts integration
                                • 2. MCP server and client setup
                                  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

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

                                          1. Which factor MOST directly affects API cost?

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


                                          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.
                                          Your team's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting - sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them. What is the most effective way to ensure all generated code is consistently formatted?

                                          A) Split the formatting rules into path-scoped .claude/rules/files that load when Claude works on matching file types.
                                          B) Extract the formatting rules into a dedicated skill that Claude loads automatically when generating code, with more detailed examples of correct formatting.
                                          C) Configure a PostToolUse hook with an Edit|Write matcher that automatically runs Prettier on each file Claude modifies.
                                          D) Add a Stop hook with a prompt-based check that evaluates whether generated code follows formatting standards and prompts Claude to fix violations.


                                          3. 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 agent has spent 25 minutes exploring a game engine's rendering subsystem -reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
                                          What's the most effective approach?

                                          A) Summarize key rendering findings, then spawn a sub-agent for physics exploration with that summary in its initial context.
                                          B) Continue in the current context with more targeted prompts referencing the specific classes by name.
                                          C) Spawn a sub-agent to explore physics independently, then manually synthesize its findings with the rendering knowledge accumulated in the main conversation.
                                          D) Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project's CLAUDE.md.


                                          4. The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?

                                          A) The coordinator's allowedTools configuration doesn't include "Task", so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.
                                          B) The coordinator's max_tokens setting is too low, causing the Task tool invocation to be truncated before the subagent type parameter can be specified.
                                          C) The AgentDefinitions are configured correctly, but the coordinator's system prompt doesn't explicitly list the available subagent types, preventing the model from knowing they can be invoked.
                                          D) Subagent context isolation means task descriptions from the coordinator don't automatically reach subagents; you need to configure explicit context forwarding in ClaudeAgentOptions.


                                          5. Your control_device tool manages smart home devices through external APIs. When a device doesn't respond within the timeout period, the tool returns an error. Production logs show that the agent simply tells users "the device is not responding" without offering helpful next steps. Which error response structure would best enable the agent to provide useful follow-up?

                                          A) Set is_error: true with a brief "Device offline" message and provide a separate tool the agent can call to retrieve context-specific troubleshooting suggestions.
                                          B) Set is_error: true with a message explaining the likely cause and suggesting troubleshooting steps the agent can offer the user.
                                          C) Set is_error: false with an optimistic message indicating the command was dispatched successfully but device acknowledgment is still pending.
                                          D) Set is_error: true with a structured technical error containing the device ID, timeout duration, and raw API response code for debugging purposes.


                                          Solutions:

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

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