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Anthropic CCA-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering & Structured Output | 20% | - Instruction design
|
| Topic 2: Context Management & Reliability | 15% | - System reliability
|
| Topic 3: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP)
|
| Topic 4: Agentic Architecture & Orchestration | 27% | - Multi-agent orchestration
|
| Topic 5: Claude Code Configuration & Workflows | 20% | - CI/CD and workflow integration
|
Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:
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 order details are added to the conversation and the model reasons about which action to take.
C) The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
D) The orchestration layer automatically routes to the next tool based on the order's status field.
2. Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?
A) Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability.
B) Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.
C) Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review.
D) Return fields organized into verified and needs_verification objects based on confidence thresholds.
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 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?
A) As a detailed instruction block in the project's root CLAUDE.md file
B) In the project's.claude/settings.json using a skillOverrides entry to register and define the workflow
C) In ~/.claude/skills/migrate-component/SKILL.md on each developer's machine
D) In .claude/skills/migrate-component/SKILL.md at the project root, committed to version control
4. During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process _refund immediately - but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist. Which change directly addresses the root cause of the agent fabricating the order_id value?
A) Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.
B) Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
C) Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
D) Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
5. An architect needs Claude to produce structured JSON that downstream software can parse automatically. Which prompting strategy is BEST?
A) Ask Claude to "respond normally."
B) Explicitly specify the required JSON schema and formatting rules.
C) Use multiple unrelated examples.
D) Increase maximum output tokens only.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: B |

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