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Anthropic CCA-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering & Structured Output | 20% | - Reliable structured generation
|
| Claude Code Workflows & Configuration | 20% | - Claude Code operational patterns
|
| Agentic Architecture & Orchestration | 27% | - System orchestration patterns
|
| Context Management & Reliability | 15% | - Long-context optimization
|
| Tool Design & MCP Integration | 18% | - Model Context Protocol (MCP)
|
Anthropic Claude Certified Architect Foundations (CCA-F) Sample Questions:
1. After integrating a local MCP server providing code analysis tools (analyze_dependencies, find_dead_code, calculate _complexity), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
MCP: analyze_ dependencies - "Analyzes dependency graph"
Built-in: Grep - "Search file contents for a pattern using regular
expressions. Returns matching lines with line numbers and surrounding
context."
What's the most effective approach to improve the agent's selection of MCP tools?
A) Expand MCP tool descriptions to detail capabilities and outputs - e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."
B) Split analyze_dependencies into granular tools ( list_imports, resolve_transitive_deps, detect_circular_deps) so each has a focused purpose less likely to overlap with Grep.
C) Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.
D) Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.
2. Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true, "content": [{"type": "text", "text": "Operation failed"}]}. The agent cannot distinguish between error types. What's the most effective improvement?
A) Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.
B) Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
C) Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
D) Enhance error responses with structured metadata: include errorCategory (transient/validation/permission), isRetryable boolean, and a description of what caused the failure.
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 need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file. What is the most appropriate approach?
A) Use direct execution to make the change.
B) Start with extended thinking mode enabled to ensure thorough reasoning about the validation logic.
C) Enter plan mode first to create a detailed implementation strategy before making the change.
D) Enter plan mode to analyze how the validation might impact other parts of the reservation flow.
4. An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?
A) Max tokens
B) Temperature
C) Prompt length
D) Context window
5. A developer includes multiple unrelated tasks inside one extremely long prompt. What is the MOST likely outcome?
A) Improved reasoning.
B) Increased prompt clarity.
C) Faster inference.
D) Reduced instruction clarity.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: D |






