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Anthropic CCAR-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Claude Code Configuration & Workflows | 20% | - Claude Code
|
| Agentic Architecture & Orchestration | 27% | - Agentic architecture patterns
|
| Context Management & Reliability | 15% | - Context handling
|
| Prompt Engineering & Structured Output | 20% | - Prompt design
|
| Tool Design & MCP Integration | 18% | - Tool integration
|
Anthropic Claude Certified Architect - Foundations Sample Questions:
Question 1
The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate--it generates messages such as, "I'll ask the web-search agent to find sources on this topic"--but no subagent execution occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?
A. The coordinator's allowedTools configuration does not include "Agent"--called "Task" in older SDK releases--so it cannot invoke the tool required to spawn subagents.
B. Subagent context isolation prevents task descriptions from reaching subagents unless explicit context forwarding is configured in ClaudeAgentOptions.
C. The AgentDefinition objects are configured correctly, but the coordinator's system prompt does not explicitly list the available subagent types.
D. The coordinator's max_tokens setting is too low, causing the subagent invocation to be truncated before the agent-type parameter is specified.
Question 2
You built an LLM-powered code-review tool that analyzes pull requests and returns structured findings. Each finding is a JSON object containing file_path, line_number, issue_category--such as security or style--and description. Developers can dismiss findings they consider unhelpful, and currently 35% of findings are dismissed. You want to analyze these dismissals to understand what the system is getting wrong and improve the prompts accordingly. What change to the output structure would best support this analysis?
A. Add a detected_pattern field recording the specific code construct that triggered the finding, such as single-letter loop variable.
B. Add a model_confidence field from 0.0 to 1.0 and filter findings below a threshold calibrated against historical dismissal rates.
C. Expand the description field with more detailed explanations of why each issue matters and how it should be fixed.
D. Remove the issue_category field and track dismissal rates only at the individual-finding level.
Question 3
You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction pipeline occasionally receives responses that cannot be parsed as valid JSON, causing downstream processing failures. The current implementation prompts Claude to return JSON in the response text and then parses it.
What is the most reliable approach to ensure Claude returns valid, schema-compliant structured data?
A. Add explicit formatting instructions to the prompt with JSON examples, emphasizing that Claude must return only valid JSON with no surrounding text.
B. Use regular expressions to locate and extract JSON from the response text, handling cases where Claude includes explanatory text around the JSON block.
C. Implement a retry loop that catches JSON parsing errors and re-prompts Claude with the error details, asking it to correct the malformed output.
D. Define a tool with a JSON schema specifying the expected structure, using tool use to constrain Claude's output to schema-compliant JSON.
Question 4
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 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.
D. Use Grep to search for "invalidate" and "expire" patterns across all files, then Read only those specific line ranges with minimal surrounding context.
Question 5
An enterprise wants Claude responses to remain professional across every interaction. Where should tone instructions primarily reside?
A. Retrieved documents
B. User prompt only
C. System prompt
D. API key description
Solutions:
| Question 1 Answer: A | Question 2 Answer: A | Question 3 Answer: D | Question 4 Answer: B | Question 5 Answer: C |








