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Anthropic CCAR-F Exam Syllabus Topics:

SectionWeightObjectives
Claude Code Configuration & Workflows20%- CI/CD integration and non-interactive mode parameters
- Path-specific rules and .claude/rules/ configuration
- CLAUDE.md hierarchy, precedence and @import rules
- Custom slash commands and plan mode vs direct execution
- Hooks vs advisory instructions
Agentic Architecture & Orchestration27%- Agentic loop design and stop_reason handling
- Session state management and workflow enforcement
- Multi-agent patterns: coordinator-subagent and hub-and-spoke
- Task decomposition and dynamic subagent selection
- Error recovery, guardrails and safety patterns
Prompt Engineering & Structured Output20%- Explicit criteria definition and few-shot prompting
- Validation, parsing and retry loop strategies
- System prompt design and persona alignment
- JSON schema design and structured output enforcement
Context Management & Reliability15%- Idempotency, consistency and failure resilience
- Context pruning and summarization strategies
- Context window optimization and prioritization
- Token budget management and cost control
Tool Design & MCP Integration18%- MCP tool, resource and prompt implementation
- Model Context Protocol (MCP) architecture and JSON-RPC 2.0
- Tool distribution and permission controls
- Tool schema design and interface boundaries
- Error handling and tool response formatting

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Anthropic Claude Certified Architect - Foundations Sample Questions (Q57-Q62):

NEW QUESTION # 57
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.
When the agent calls lookup_order and receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?

Answer: A

Explanation:
In the standard Claude tool-use loop, the application executes lookup_order and sends its output back as a tool_result . That result becomes part of the conversation state available to Claude. Claude then evaluates the purchase date, refund policy, customer request, authorization constraints, and available tools before selecting the next action.
Anthropic describes client-tool orchestration as a repeated loop: Claude emits a tool_use request, the application executes it, returns a tool_result , and Claude continues reasoning from the updated conversation.
Claude, rather than the tool implementation, selects when and how to invoke the next available tool unless the application has explicitly implemented a fixed workflow. ( https://platform.claude.com/docs/en/agents-and- tools/tool-use/how-tool-use-works ) Options B and C describe possible custom orchestration architectures, but neither is stated in the scenario.
Option D is inconsistent with adaptive agent behavior because later actions depend on information that did not exist before lookup_order completed. A rigid sequence would not respond appropriately to different purchase dates, eligibility states, or order conditions.
The tool result should return high-signal fields such as purchase date, return-window status, refund eligibility, existing refund status, and stable order identifiers so Claude can make the subsequent decision accurately.
Official references/topics: Tool-result continuation, adaptive agent loops, model-directed tool selection, sequential dependency handling.


NEW QUESTION # 58
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 frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing /migrate-component. The workflow should stay in sync as the team iterates on it. Where should you place the skill file?

Answer: D

Explanation:
A project-scoped skill is available to everyone working in that repository and stays synchronized through version control. The skill directory name exposes it as /migrate-component.


NEW QUESTION # 59
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, and Glob-and integrates with Model Context Protocol (MCP) servers.
Engineers frequently ask the agent to cross-reference code changes with Jira tickets during reviews- checking ticket descriptions, acceptance criteria, and recent comments. This currently requires manually copying and pasting content into conversations. The team wants the agent to access this standard Jira ticket data directly.
What is the most effective approach?

Answer: C

Explanation:
Option D uses the established integration mechanism without creating unnecessary infrastructure. Anthropic's Claude Code MCP documentation specifically recommends connecting an MCP server when users repeatedly copy information from an external system, such as an issue tracker, into conversations. Once connected, the server exposes Jira operations through named, schema-defined tools that Claude can discover and invoke directly. This allows the agent to retrieve ticket descriptions, acceptance criteria, comments, and metadata while preserving the server's authentication and access controls.
Option A exposes authentication details to shell commands and requires the agent to construct requests and parse responses repeatedly. Option B is justified only when no existing server provides the required capabilities or the workflow requires highly specialized operations. The question requires standard Jira information, so building and maintaining another server is unnecessary. Option C creates stale, manually synchronized copies that lose permissions, current comments, and authoritative ticket state. The team should therefore select a trusted Jira MCP implementation, configure appropriate read-only scopes where possible, and verify its tool descriptions and data-access boundaries before enabling it.


NEW QUESTION # 60
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: A

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 # 61
A customer sends: "This is frustrating. I've explained my issue twice and nothing is being resolved. I want to talk to a real person NOW." The agent has not yet called any tools to investigate the customer's account.
What should the agent do?

Answer: B

Explanation:
Option C respects the customer's explicit request for human assistance. The agent should not attempt to retain the interaction, gather additional account information, or require the customer to repeat the request. Because the escalation decision has already been made by the customer, further autonomous investigation would create unnecessary delay and disregard clear human direction.
Anthropic's trustworthy-agent framework emphasizes maintaining meaningful human control over agent autonomy. Anthropic's customer-support implementation guidance also recommends defining interaction branches and measuring escalation efficiency as part of the system's success criteria.
Option A deliberately delays escalation despite an unambiguous request. Option B performs unnecessary tool calls and may expose or retrieve account information that is not required before transferring the conversation.
Option D similarly introduces another conversational barrier. Since the current exchange is short and contains no tool-generated evidence, passing the available conversation history gives the human agent the immediate context-the customer's frustration, repeated unsuccessful attempts, and explicit transfer request-without pretending that account investigation has occurred.


NEW QUESTION # 62
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