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Anthropic CCA-F Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Tool Design & MCP Integration | 18% | - Tool definition and best practices
- 1. Error handling and validation
- 2. Tool descriptions and selection logic
- Model Context Protocol (MCP)
- 1. Tools, resources, and prompts integration
- 2. MCP server and client setup
|
| Topic 2: Context Management & Responsible AI | 15% | - Context window optimization
- 1. Token management and truncation strategies
- 2. Context retention and summarization
- Safety and compliance
- 1. Refusal handling and risk mitigation
- 2. Constitutional AI principles
|
| Topic 3: Agentic Architecture & Orchestration | 27% | - 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 4: Claude Code Configuration & Workflows | 20% | - CI/CD and development integration
- 1. Plan mode vs direct execution
- 2. Pipeline automation and review
- Configuration files and structure
- 1. CLAUDE.md and rules system
- 2. Custom commands and workflows
|
| Topic 5: Prompt Engineering & Structured Output | 20% | - 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
|
Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:
1. A legal department requires Claude to answer only when sufficient evidence exists in retrieved documents. What should Claude do if evidence is insufficient?
A) Increase creativity.
B) Clearly indicate insufficient information.
C) Invent a likely answer.
D) Search social media automatically.
2. Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?
A) Return is_error: true with the original message content echoed back.
B) Return is_error: true with a message encouraging retry: "Delivery service temporarily unavailable.
Please retry the notification."
C) Return is_error: true with a structured field retry_safe: true for timeouts, distinguishing them from permanent failures that should not be retried.
D) Return is_error: true with a message communicating uncertainty: "Timeout - status unknown.
Message may have been sent. Avoid retry."
3. The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?
A) Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
B) The prompt lacks instructions telling Claude to remember information across multiple exchanges.
C) The verification tool is clearing the agent's internal state after each successful validation step.
D) The conversation history isn't being passed in subsequent API requests.
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 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.
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 coordinator's allowedTools configuration doesn't include "Task", so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.
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. After the web search agent and document analysis agent complete their tasks, the coordinator invokes the synthesis agent. However, the synthesis agent responds that it cannot complete the task because no research findings were provided. What is the most likely cause of this issue?
A) The synthesis agent's context window is not large enough to hold the combined outputs from both previous agents.
B) The coordinator did not include the outputs from the previous agents in the synthesis agent's prompt.
C) The subagents need to share a single API connection to enable automatic context sharing between invocations.
D) The synthesis agent needs tools that can fetch results directly from the other agents' conversation histories.
Solutions:
Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: B |