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Updated: Jul 19, 2026
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| Certification Vendor: | Anthropic |
| Exam Name: | Claude Certified Architect – Foundations |
| Exam Number: | CCA-F (also referred to as CCAR-F in some references) |
| Exam Price: | Free for first 5,000 partner employees during Early Access; $99 USD thereafter |
| Passing Score: | 720 out of 1000 scaled score |
| Certificate Validity Period: | 2 years from date of passing |
| Real Exam Qty: | 60 |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple-choice single-select, Multiple-choice multiple-select, Scenario-based questions |
| Available Languages: | English |
| Recommended Training: | Anthropic Academy Official Training Courses |
| Exam Registration: | Anthropic Partner Network Application Anthropic CCA-F Access Request & Registration |
| Sample Questions: | Anthropic CCAR-F Sample Questions |
| Exam Way: | Online remotely proctored via ProctorFree; closed-book, no external resources allowed |
| Pre Condition: | Currently restricted to employees of Anthropic Partner Network organizations. Recommended prerequisites: completion of all 200-level courses in Anthropic Academy, working familiarity with Claude Agent SDK, Claude Code, Anthropic API and Model Context Protocol (MCP), plus at least 6 months of hands-on experience building production solutions with Claude technologies. |
| Official Syllabus URL: | https://anthropic.skilljar.com/claude-certified-architect-foundations-access-request |
| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Idempotency, consistency and failure resilience - Token budget management and cost control - Context window optimization and prioritization - Context pruning and summarization strategies |
| 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 |
| Agentic Architecture & Orchestration | 27% | - Task decomposition and dynamic subagent selection - Agentic loop design and stop_reason handling - Session state management and workflow enforcement - Multi-agent patterns: coordinator-subagent and hub-and-spoke - Error recovery, guardrails and safety patterns |
| Tool Design & MCP Integration | 18% | - Tool distribution and permission controls - Tool schema design and interface boundaries - Error handling and tool response formatting - Model Context Protocol (MCP) architecture and JSON-RPC 2.0 - MCP tool, resource and prompt implementation |
| Claude Code Configuration & Workflows | 20% | - Hooks vs advisory instructions - CLAUDE.md hierarchy, precedence and @import rules - Custom slash commands and plan mode vs direct execution - Path-specific rules and .claude/rules/ configuration - CI/CD integration and non-interactive mode parameters |
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) Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.
B) Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human .
C) Call escalate_to_human , passing only the customer's original message.
D) Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
2. 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 system must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON.
What approach provides the most reliable schema compliance?
A) Append instructions like "Output only valid JSON matching the schema exactly" and implement retry logic to re-prompt when JSON parsing fails.
B) Include detailed JSON formatting instructions and the target schema in your prompt, then parse Claude' s text response as JSON.
C) Define a tool with your target schema as input parameters and have Claude call it with the extracted data.
D) Pre-fill Claude's response with an opening brace to force JSON output, then complete and parse the response.
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.
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 ~/.claude.json and the playlist server to .mcp.json .
B) Add both servers to the project-level .mcp.json file.
C) Add the venue server to .mcp.json and the playlist server to ~/.claude.json .
D) Add both servers to your local ~/.claude.json .
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.
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) 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.
C) 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.
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.
5. 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's exploration subagent spent 30 minutes analyzing a legacy payment system, reading 47 files and documenting data flows. The session was interrupted when the engineer's connection dropped. While away, a teammate merged a PR that renamed two utility functions. The engineer wants to continue the same exploration.
What's the most effective approach?
A) Resume the subagent from its previous transcript and inform it about the renamed functions.
B) Launch a fresh subagent and include the prior transcript in the initial prompt for context.
C) Launch a fresh subagent with a summary of prior findings.
D) Resume the subagent from its previous transcript without mentioning the changes-the architecture understanding remains valid.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: A |
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