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Anthropic CCAR-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Error handling and tool response formatting - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Tool distribution and permission controls |
| Topic 2: Prompt Engineering & Structured Output | 20% | - JSON schema design and structured output enforcement - Validation, parsing and retry loop strategies - Explicit criteria definition and few-shot prompting - System prompt design and persona alignment |
| Topic 3: Context Management & Reliability | 15% | - Token budget management and cost control - Context pruning and summarization strategies - Context window optimization and prioritization - Idempotency, consistency and failure resilience |
| Topic 4: Claude Code Configuration & Workflows | 20% | - CLAUDE.md hierarchy, precedence and @import rules - Path-specific rules and .claude/rules/ configuration - Hooks vs advisory instructions - Custom slash commands and plan mode vs direct execution - CI/CD integration and non-interactive mode parameters |
| Topic 5: Agentic Architecture & Orchestration | 27% | - Error recovery, guardrails and safety patterns - 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 |
Anthropic Claude Certified Architect - Foundations Sample Questions:
A team wants to reduce hallucinations without significantly changing application logic. Which improvement should be implemented FIRST?
- A. Remove role instructions.
- B. Shorten all outputs.
- C. Raise temperature.
- D. Add reliable retrieval sources.
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline processes restaurant menus and must output structured JSON with fields for item names, descriptions, prices, and dietary tags. Some menus use inconsistent formatting-- prices as "$12" vs "12.00", dietary info as icons vs text.
What's the most reliable approach?
- A. Request multiple extraction attempts per document and select the most common format.
- B. Extract data as-is and normalize formats in post-processing code after Claude returns.
- C. Define a strict output schema and include format normalization rules in your prompt.
- D. Use separate extraction calls for each field to ensure consistent handling of each type.
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
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.
An engineer sees an unfamiliar error message "SYNC_CONFLICT: entity version mismatch detected" in production logs but doesn't know which of the 12 services in the codebase generates it. They ask the agent to help locate the source code. What exploration approach will most efficiently find the responsible code?
- A. Use Grep to search for distinctive text from the error message (like "SYNC_CONFLICT" or "entity version mismatch"), then Read the matching files to understand context.
- B. Use Glob to find files in directories commonly associated with error handling (such as errors/, exceptions/,or handlers/) across all services, then Read each matching file.
- C. Read the project's README and service configuration files to understand the architecture, then systematically Read source files in each service directory.
- D. Use Grep to find all files that import the project's error handling module, then Read those files to locate custom error definitions.
Explanation: Only visible for Pass4suresVCE members. You can sign-up / login (it's free).
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 has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response) in addition to tools. How do these MCP prompts become accessible within Claude Code?
- A. They are surfaced as @-mentionable resources alongside files, fetched and attached to your message when referenced.
- B. They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session.
- C. They appear as slash commands (e.g., mcp_servername_deploy_checklist) that you can invoke, with arguments passed after the command name.
- D. They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task.
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Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like 'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order, which catches all exceptions and returns a tool_result with is_error:
true and the message "Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools. Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?
- A. Implement retry logic with exponential backoff inside each tool implementation so transient errors are resolved transparently within the tool before any failure result is surfaced to Claude in the agentic loop.
- B. Add an error classification step in the agentic loop that intercepts tool errors before Claude sees them, tags each as "retry," "try_alternative," or "escalate," and appends that recommendation to the tool result.
- C. Return error-type-specific messages with is_error: true, e.g., "Order not found-try get_customer to search by phone" for data errors and "Database timeout (transient)-retry should succeed" for infrastructure errors.
- D. Remove is_error: true and return the error details as normal tool content, so Claude reasons about the response as data rather than treating it as a flagged failure condition that biases retry behavior.
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