Anthropic CCAR-F : Claude Certified Architect - Foundations

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Anthropic CCAR-F Exam Syllabus Topics:

Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

Your infrastructure-as-code repository includes Terraform modules (/terraform/), Kubernetes manifests (/kubernetes/), and CI/CD pipeline scripts (/pipelines/). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?

A. Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*"]), loading rules only when editing matching files.
B. Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g, "## Terraform Conventions"), improving organization and readability.
C. Split content into subdirectory CLAUDE.md files (/terraform/CLAUDE.md,
/kubernetes/CLAUDE.md), so Claude loads directory-specific guidance.
D. Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.

Question 2

A retail organization observes that Claude occasionally gives different wording when summarizing identical customer reviews. The summaries remain accurate. What is the MOST likely explanation?

A. Retrieval failed.
B. The API returned cached responses.
C. Claude is malfunctioning.
D. Large language models are probabilistic.

Question 3

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 extraction pipeline processes invoices and extracts line items, subtotals, tax amounts, and grand totals. During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total--sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?

A. Add few-shot examples demonstrating invoices where extracted line items sum correctly to the stated total, encouraging the model to produce mathematically consistent extractions.
B. Extract line items and totals independently, then use a separate validation model to reconcile discrepancies by determining which extracted values are most likely correct.
C. Implement post-processing that automatically adjusts line item amounts proportionally when their sum doesn't match the stated total.
D. Add a "calculated_total" field where the model sums extracted line items alongside a
"stated_total" field. Flag records for human review when values differ.

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 sees an unfamiliar error message "SYNC_CONFLICT: entity version mismatch detected" in production logs but doesn't know which of the 12 services in the codebase generates it. They ask the agent to help locate the source code. What exploration approach will most efficiently find the responsible code?

A. Use Grep to find all files that import the project's error handling module, then Read those files to locate custom error definitions.
B. Use Grep to search for distinctive text from the error message (like "SYNC_CONFLICT" or "entity version mismatch"), then Read the matching files to understand context.
C. Use Glob to find files in directories commonly associated with error handling (such as errors/, exceptions/,or handlers/) across all services, then Read each matching file.
D. Read the project's README and service configuration files to understand the architecture, then systematically Read source files in each service directory.

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. Add few-shot examples to your prompt demonstrating correct handling of informal measurements--extracting them verbatim rather than converting or omitting them.
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. Fine-tune the model on the 847 corrected extractions.
D. Update your JSON schema to add a "measurement_type" enum field (precise/informal).

Solutions: