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Anthropic CCA-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP)
|
| Topic 2: Context Management & Reliability | 15% | - Context window optimization
|
| Topic 3: Agentic Architecture & Orchestration | 27% | - Agentic loops & lifecycle design
|
| Topic 4: Claude Code Configuration & Workflows | 20% | - CI/CD and workflow integration
|
| Topic 5: Prompt Engineering & Structured Output | 20% | - Tool-augmented prompting
|
Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:
1. The agent verifies customer identity through a multi-step process before resetting passwords.
During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened. What's the most likely cause of this behavior?
A) The prompt lacks instructions telling Claude to remember information across multiple exchanges.
B) The verification tool is clearing the agent's internal state after each successful validation step.
C) Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
D) The conversation history isn't being passed in subsequent API requests.
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.
You're implementing a new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py, error_handlers.py, and audit_logger.py. This is a one-off integration task - these patterns are well-documented in your team wiki and don't need additional project-level documentation. What's the most effective approach?
A) Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.
B) Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow.
C) Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.
D) Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.
3. 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 the Read tool to sequentially load all 15 files, building complete understanding across the full caching implementation.
B) Analyze imports and class hierarchies to identify the base cache class, Read that file to understand the interface, then trace specific invalidation implementations.
C) Use Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context.
D) 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.
4. An architect needs Claude to produce structured JSON that downstream software can parse automatically. Which prompting strategy is BEST?
A) Use multiple unrelated examples.
B) Explicitly specify the required JSON schema and formatting rules.
C) Ask Claude to "respond normally."
D) Increase maximum output tokens only.
5. Your order management system requires tools for three distinct operations: issuing refunds (requires amount and reason), canceling orders (requires reason), and requesting reshipments (requires shipping address). Each operation shares an order_id parameter but has different additional requirements. You notice during testing that with your current unified tool design, the agent frequently omits required parameters or includes irrelevant ones. What design change will most effectively improve parameter accuracy?
A) Keep one unified tool with a nested operation_details object parameter whose internal structure varies by operation type, documented in the tool description.
B) Split into three separate tools (issue_refund, cancel_order, request_reshipment), each defining only the parameters required for that specific operation.
C) Keep one unified tool with all parameters marked optional, but add detailed few-shot examples in the system prompt showing correct parameter combinations for each operation type.
D) Keep one unified tool but add JSON Schema if-then-else conditionals to enforce that parameters like amount are required only when the operation type is "refund".
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: B |
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