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Anthropic CCAR-F Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Context Management & Reliability | 15% | - Managing context windows and information flow - Production deployment considerations - Evaluation and reliability strategies |
| Agentic Architecture & Orchestration | 27% | - Designing agentic systems and workflows - Agent coordination and orchestration patterns - Selecting appropriate Claude architectures |
| Tool Design & MCP Integration | 18% | - Tool safety, reliability, and usability - Designing effective tools for Claude applications - Model Context Protocol (MCP) concepts and integration |
| Prompt Engineering & Structured Output | 20% | - Structured output generation and validation - Prompt design strategies - Improving Claude response quality and consistency |
| Claude Code Configuration & Workflows | 20% | - Integrating Claude Code into development processes - Claude Code usage and configuration - Developer productivity workflows |
Anthropic Claude Certified Architect - Foundations Sample Questions:
Question 1
A chatbot frequently receives greetings, policy questions, and technical support requests. Which architecture improves maintainability?
A. Maximum temperature.
B. Disable retrieval.
C. Prompt routing based on request type.
D. One prompt for every scenario.
Question 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've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?
A. Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
B. Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
C. Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.
D. Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.
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 uses tool use with a JSON schema in which property_type is defined as an enum:
house, apartment, condo, or townhouse. After deployment, 8% of extractions fail schema validation. Investigation reveals that listings mention many uncommon property types--"studio,"
"loft," "duplex," "mobile home," "tiny house," and "converted warehouse"--and new types continue appearing regularly.
What is the most effective long-term solution?
A. Change property_type from an enum to a free-form string and implement a normalization step in post-processing.
B. Add few-shot examples demonstrating how to map unexpected property types to the closest existing enum value.
C. Continuously expand the enum to include newly observed property types and add monitoring for additional edge cases.
D. Add an other value to the enum with a separate property_type_detail string field for specifics when other is selected.
Question 4
Your automated review generates many findings per pull request, but developer feedback shows that roughly half are dismissed as "not worth addressing." Analysis reveals that dismissed findings are often technically accurate but involve minor style preferences or patterns that are acceptable in your codebase. Before adding infrastructure complexity, what prompt-design change could most effectively reduce dismissals while maintaining the detection of genuine issues?
A. Append instructions telling Claude to "only report findings you are highly confident are genuine problems."
B. Implement a secondary classification model that filters Claude's findings according to predicted developer acceptance.
C. Ask Claude to rate every finding's confidence from 1 to 10 and include only findings rated 8 or higher.
D. Add explicit criteria defining which issues to report, such as bugs and security defects, and which issues to skip, such as minor style preferences and accepted local patterns.
Question 5
A healthcare company processes long clinical reports. Some exceed Claude's practical context requirements. What is the BEST architectural approach?
A. Ignore the excess text.
B. Increase temperature.
C. Chunk documents before processing.
D. Randomly remove paragraphs.
Solutions:
| Question 1 Answer: C | Question 2 Answer: D | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: C |

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