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Anthropic CCA-F Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Agentic Architecture & Orchestration | 27% | - Anthropic Agent SDK usage
- 1. Session state and context handling
- 2. Task spawning and tool management
- Agent design patterns
- 1. Agent loops and control flow
- 2. Hub-and-spoke multi-agent systems
|
| Topic 2: Claude Code Configuration & Workflows | 20% | - CI/CD and development integration
- 1. Pipeline automation and review
- 2. Plan mode vs direct execution
- Configuration files and structure
- 1. CLAUDE.md and rules system
- 2. Custom commands and workflows
|
| Topic 3: Prompt Engineering & Structured Output | 20% | - Advanced prompting techniques
- 1. System prompts and role framing
- 2. Few-shot and chain-of-thought prompting
- Structured data generation
- 1. Output validation and reliability
- 2. JSON schema enforcement
|
| Topic 4: Context Management & Responsible AI | 15% | - Context window optimization
- 1. Token management and truncation strategies
- 2. Context retention and summarization
- Safety and compliance
- 1. Refusal handling and risk mitigation
- 2. Constitutional AI principles
|
| Topic 5: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP)
- 1. Tools, resources, and prompts integration
- 2. MCP server and client setup
- Tool definition and best practices
- 1. Error handling and validation
- 2. Tool descriptions and selection logic
|
Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:
1. An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?
A) Max tokens
B) Context window
C) Temperature
D) Prompt length
2. 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 asks the agent to understand how the caching layer works before adding a new cache invalidation trigger. After initial Grep searches, the agent has identified that caching logic spans 15 files including decorators, middleware, and service classes (~8,000 lines total). What's the most effective next step for building understanding while managing context constraints?
A) Use Glob to find files matching common caching patterns (cache.py, caching/), prioritize the largest files by reading them first, then check smaller files for gaps.
B) Use Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context.
C) Analyze imports and class hierarchies to identify the base cache class, Read that file to understand the interface, then trace specific invalidation implementations.
D) Use the Read tool to sequentially load all 15 files, building complete understanding across the full caching implementation.
3. An architect needs Claude to produce structured JSON that downstream software can parse automatically. Which prompting strategy is BEST?
A) Increase maximum output tokens only.
B) Ask Claude to "respond normally."
C) Use multiple unrelated examples.
D) Explicitly specify the required JSON schema and formatting rules.
4. 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) 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.
B) Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
C) Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and trigger escalation when the frustration score exceeds a configured threshold.
D) 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.
5. What is the PRIMARY purpose of few-shot prompting?
A) Reduce token costs.
B) Encrypt prompts.
C) Demonstrate desired output patterns.
D) Increase model size.
Solutions:
Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: C |