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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

An organization wants predictable outputs for automated invoice classification. Which API parameter should generally be LOWER?

A. Context window
B. Prompt length
C. Temperature
D. Max tokens

Question 2

Which statement BEST describes Retrieval-Augmented Generation?

A. It changes token pricing.
B. It permanently retrains Claude.
C. It compresses model weights.
D. It supplies relevant external knowledge during inference.

Question 3

Which statement BEST describes Retrieval-Augmented Generation?

A. It changes token pricing.
B. It permanently retrains Claude.
C. It compresses model weights.
D. It supplies relevant external knowledge during inference.

Question 4

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.
A critical bug is affecting production users. Error logs show exceptions in the OrderProcessing module with a clear stack trace pointing to a specific function. You haven't worked with this module before. What's the most effective approach?

A. Enter plan mode to explore the module's architecture and dependencies before attempting any fixes.
B. Use direct execution to examine the stack trace, read the relevant code, and implement a fix once you identify the root cause.
C. Start with direct execution to gather initial information, then switch to plan mode to design a comprehensive solution before implementing any changes.
D. Use plan mode to analyze the error in context of the module's design, enumerate potential root causes, and prioritize fixes systematically.

Question 5

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your system has been running for 3 weeks and human reviewers have corrected 847 extractions.
Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or
"a splash," the model either invents specific amounts or leaves fields empty--accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

A. Update your JSON schema to add a "measurement_type" enum field (precise/informal).
B. Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.
C. Add few-shot examples to your prompt demonstrating correct handling of informal measurements--extracting them verbatim rather than converting or omitting them.
D. Fine-tune the model on the 847 corrected extractions.

Solutions: