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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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 caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires.
What's the most effective way to start your iterative workflow?

A) Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
B) Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
C) Use plan mode to analyze the current /products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.
D) Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.

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 is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing.
Which configuration approach correctly applies MCP server scopes?

A) Add the venue server to .mcp.json and the playlist server to ~/.claude.json .
B) Add the venue server to ~/.claude.json and the playlist server to .mcp.json .
C) Add both servers to the project-level .mcp.json file.
D) Add both servers to your local ~/.claude.json .

3. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
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) Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and triggers escalation when the frustration score exceeds a configured threshold.
B) 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.
C) 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.
D) Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.

4. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools (get_customer, lookup_order, process_refund, escalate_to_human). Your target is 80%+ first-contact resolution while knowing when to escalate.
A customer returns 4 hours after their initial session about the same billing dispute. The previous 32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution: 24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information.
What approach most reliably handles returning customers?

A) Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.
B) Resume with full history and configure the agent to automatically re-call all previously used tools at session start to ensure data freshness.
C) Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.
D) Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.

5. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API.
What is the primary mechanism your application uses to determine whether to continue the loop or stop?

A) You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
B) You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
C) You check the stop_reason field in each API response-the loop continues while it equals "tool_use" and exits when it changes to "end_turn" or another terminal value.
D) You check whether Claude's response contains a text content block-if text is present, the agent has produced its final answer and the loop should exit.

Solutions: