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Anthropic CCA-F Exam Syllabus Topics:

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

1. 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 message explaining the likely cause and suggesting troubleshooting steps the agent can offer the user.
B) Set is_error: true with a structured technical error containing the device ID, timeout duration, and raw API response code for debugging purposes.
C) 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.
D) Set is_error: false with an optimistic message indicating the command was dispatched successfully but device acknowledgment is still pending.

2. Production logs show that when the agent handles complex billing disputes requiring 6+ tool calls, it sometimes exhausts its max_turns limit after gathering data but before completing resolution or escalating. The team's goal is to guarantee that every customer interaction ends with either a completed resolution or a human handoff, regardless of how the agent loop terminates. Which approach achieves this guarantee?

A) Split the workflow into two sequential agent invocations - a first agent gathers information via get_customer and lookup_order, then a second agent receives that data and handles process_refund or escalate_to_human, each with separate turn budgets.
B) Add orchestration-layer code that checks the agent's outcome after each loop termination - if the loop ended without a completed resolution or escalation, programmatically call escalate_to_human with the accumulated conversation context and tool results.
C) Add system prompt instructions telling the agent to call escalate_to_human with a summary of its findings whenever it determines it cannot complete resolution within its remaining actions.
D) Implement a pre-tool-use hook that counts tool invocations and terminates the loop with an automatic escalation once the agent reaches 80% of its max_turns limit.

3. The web search agent has gathered several relevant sources for a research topic. The document analysis agent now needs to examine these sources. How does information typically flow between these two specialized subagents?

A) Both agents access a shared memory store where the web search agent writes findings and the document analysis agent reads them.
B) The coordinator agent receives the web search agent's output and includes relevant findings in the prompt when invoking the document analysis agent.
C) The web search agent directly invokes the document analysis agent, passing the discovered sources as parameters.
D) The agents communicate through an event-driven message queue, with the document analysis agent subscribing to web search completion events.

4. A chatbot frequently receives greetings, policy questions, and technical support requests. Which architecture improves maintainability?

A) Disable retrieval.
B) One prompt for every scenario.
C) Prompt routing based on request type.
D) Maximum temperature.

5. 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're implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude. What approach will most effectively enable progressive improvement across multiple iterations?

A) Provide Claude with a detailed natural language specification of the algorithm, including all requirements and edge cases. Review each output manually and provide descriptive feedback on what behavior needs to change.
B) Write a test suite covering expected behavior, edge cases, and performance requirements before implementation. Ask Claude to write code that passes the tests, then iterate by sharing test failures with each refinement request.
C) Provide Claude with a reference implementation from documentation, then ask it to rewrite the code to match your codebase style and add the required edge case handling, comparing outputs against the reference.
D) Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.

Solutions: