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Anthropic CCAR-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - System prompt design and persona alignment - JSON schema design and structured output enforcement - Explicit criteria definition and few-shot prompting - Validation, parsing and retry loop strategies |
| Claude Code Configuration & Workflows | 20% | - Path-specific rules and .claude/rules/ configuration - CLAUDE.md hierarchy, precedence and @import rules - Hooks vs advisory instructions - CI/CD integration and non-interactive mode parameters - Custom slash commands and plan mode vs direct execution |
| Agentic Architecture & Orchestration | 27% | - Agentic loop design and stop_reason handling - Error recovery, guardrails and safety patterns - Task decomposition and dynamic subagent selection - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke |
| Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Context window optimization and prioritization - Token budget management and cost control - Context pruning and summarization strategies |
| Tool Design & MCP Integration | 18% | - MCP tool, resource and prompt implementation - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - Tool schema design and interface boundaries - Error handling and tool response formatting - Tool distribution and permission controls |
Anthropic Claude Certified Architect - Foundations Sample Questions:
Question #1
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?
A. The agent executes the remaining steps in a tool sequence planned at the start of the request.
B. The orchestration layer automatically routes to the next tool based on the order's status field.
C. The order details are added to the conversation and the model reasons about which action to take.
D. The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
Question #2
A developer includes multiple unrelated tasks inside one extremely long prompt. What is the MOST likely outcome?
A. Faster inference.
B. Improved reasoning.
C. Increased prompt clarity.
D. Reduced instruction clarity.
Question #3
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 coordinator's allowedTools configuration doesn't include "Task", so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.
B. Subagent context isolation means task descriptions from the coordinator don't automatically reach subagents; you need to configure explicit context forwarding in ClaudeAgentOptions.
C. 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.
D. 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.
Question #4
The document analysis agent has a single analyze_documnet tool that takes a document and a free-text instruction parameter. During evaluation, requests like "extract the key financial metrics" often return narrative summaries, while "summarize the methodology" sometimes returns raw data tables. The synthesis agent reports that 35% of analysis results require re-requests with clarified instructions. What's the most effective way to improve reliability?
A. Split the generic tool into purpose-specific tools - extract_data_points, summarize_content, verify_claim_against_source - each with defined input/output contracts
B. Enhance the tool description with detailed examples showing how different instruction phrasings should map to different output formats
C. Have the coordinator pre-classify each analysis request before passing instructions to the document analysis agent
D. Keep the single tool but add an analyze_type enum parameter requiring explicit selection between extraction, summarization, and verification modes
Question #5
Your automated review CI jobs take 18 seconds to initialize before Claude begins analyzing code.
Profiling reveals that the delay comes from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUDE.md files throughout your monorepo. You need to reduce startup time while ensuring that reviews still enforce your team's coding standards, which are documented in the root-level CLAUDE.md file. What is the most effective approach?
A. Run in --bare mode and pass --append-system-prompt-file ./CLAUDE.md to explicitly load your project standards while skipping all automatic discovery.
B. Run in --bare mode and specify all review criteria directly in the -p prompt argument for every CI invocation, without referencing external files.
C. Replace the default prompt entirely by using --system-prompt-file ./CLAUDE.md, which bypasses default prompt assembly and loads only your project rules.
D. Keep the default initialization and add --exclude-dynamic-system-prompt-sections to reduce per- machine prompt variability and improve prompt-cache hit rates across runners.
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: D | Question #3 Correct Answer: A | Question #4 Correct Answer: A | Question #5 Correct Answer: A |


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