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Anthropic CCAR-F Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Prompt Engineering & Structured Output20%- Explicit criteria definition and few-shot prompting
- System prompt design and persona alignment
- JSON schema design and structured output enforcement
- Validation, parsing and retry loop strategies
Topic 2: Context Management & Reliability15%- Context window optimization and prioritization
- Idempotency, consistency and failure resilience
- Token budget management and cost control
- Context pruning and summarization strategies
Topic 3: Claude Code Configuration & Workflows20%- Hooks vs advisory instructions
- 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
Topic 4: Tool Design & MCP Integration18%- 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
- MCP tool, resource and prompt implementation
Topic 5: Agentic Architecture & Orchestration27%- Error recovery, guardrails and safety patterns
- Multi-agent patterns: coordinator-subagent and hub-and-spoke
- Session state management and workflow enforcement
- Agentic loop design and stop_reason handling
- Task decomposition and dynamic subagent selection

Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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.
Your agent is handling a billing dispute. After calling get_customer and lookup_order , it identifies that the dispute involves a promotional pricing error requiring manager approval-beyond the agent's authorization level.
How should the workflow handle this mid-process escalation?

A) Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human .
B) Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
C) Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.
D) Call escalate_to_human , passing only the customer's original message.


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 has three requirements for Claude Code's behavior in your project:
* Claude must never modify files in the db/migrations/ directory.
* Claude should prefer your custom logging module over console.log .
* All TypeScript files must be auto-formatted with Prettier after every edit.
All three are currently written as instructions in your project's CLAUDE.md. During a complex refactoring session, a developer discovers that Claude edited a migration file, violating requirement #1.
How should you restructure these requirements across Claude Code's configuration mechanisms?

A) Add Edit(./db/migrations/**) to permissions.deny in the project settings, keep the logging preference in CLAUDE.md, and add a PostToolUse hook to run Prettier after TypeScript edits.
B) Move all three requirements into .claude/rules/ as path-scoped rules: one targeting db/migrations/** that forbids editing those files, and others targeting **/*.ts for the logging convention and formatting instruction.
C) Rewrite all three requirements in CLAUDE.md using stronger directive language and add few-shot examples that demonstrate Claude refusing to edit migration files and running Prettier after edits.
D) Configure hooks for all three: a PreToolUse hook script that blocks Edit calls targeting db/migrations/ , a PreToolUse hook script that adds logging convention context before edits, and a PostToolUse hook that runs Prettier after TypeScript edits.


3. 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 complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude.
What approach will most effectively enable progressive improvement across multiple iterations?

A) Provide Claude with a reference implementation from documentation, then ask it to rewrite the code to match your codebase style and add the required edge case handling, comparing outputs against the reference.
B) Write a test suite covering expected behavior, edge cases, and performance requirements before implementation. Ask Claude to write code that passes the tests, then iterate by sharing test failures with each refinement request.
C) Provide Claude with a detailed natural language specification of the algorithm, including all requirements and edge cases. Review each output manually and provide descriptive feedback on what behavior needs to change.
D) Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.


4. 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.
Your agent has spent 25 minutes exploring a game engine's rendering subsystem-reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
What's the most effective approach?

A) Spawn a sub-agent to explore physics independently, then manually synthesize its findings with the rendering knowledge accumulated in the main conversation.
B) Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project's CLAUDE.md.
C) Summarize key rendering findings, then spawn a sub-agent for physics exploration with that summary in its initial context.
D) Continue in the current context with more targeted prompts referencing the specific classes by name.


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 raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

A) Implement sliding window context that retains the most recent 30 turns.
B) Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
C) Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
D) Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.


Solutions:

Question # 1
Answer: A
Question # 2
Answer: A
Question # 3
Answer: B
Question # 4
Answer: C
Question # 5
Answer: D

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