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Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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're implementing a caching layer for API responses to speed up the /products endpoint. You have a rough idea-Redis with a 5-minute TTL-but you're new to production caching and aren't sure what other considerations a robust implementation requires.
What's the most effective way to start your iterative workflow?

A) Use plan mode to analyze the current /products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.
B) Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
C) Start with a minimal request: "Add Redis caching to /products with 5-minute TTL." Add features and fix issues through follow-up prompts as problems surface during testing.
D) Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.

2. 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) 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.
C) Extract line items and totals independently, then use a separate validation model to reconcile discrepancies by determining which extracted values are most likely correct.
D) Implement post-processing that automatically adjusts line item amounts proportionally when their sum doesn't match the stated total.

3. You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools ( get_customer , lookup_order , process_refund , escalate_to_human ). Your target is 80%+ first-contact resolution while knowing when to escalate.
A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

A) Implement sliding window context that retains the most recent 30 turns.
B) Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
C) Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.
D) Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.

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.
You're implementing a complex graph traversal algorithm with specific performance requirements and edge cases to handle (disconnected nodes, cycles, weighted edges). You want to structure your workflow for efficient iterative refinement with Claude.
What approach will most effectively enable progressive improvement across multiple iterations?

A) Provide Claude with a reference implementation from documentation, then ask it to rewrite the code to match your codebase style and add the required edge case handling, comparing outputs against the reference.
B) Write a test suite covering expected behavior, edge cases, and performance requirements before implementation. Ask Claude to write code that passes the tests, then iterate by sharing test failures with each refinement request.
C) Provide Claude with a detailed natural language specification of the algorithm, including all requirements and edge cases. Review each output manually and provide descriptive feedback on what behavior needs to change.
D) Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.

5. 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.
Your team wants Claude to follow a detailed code review checklist (8 items covering API changes, test coverage, documentation, security, etc.) when reviewing pull requests. The team also uses Claude extensively for other tasks: writing new features, debugging production issues, and generating documentation. Currently, developers paste the checklist at the start of each review session.
Which approach best addresses this workflow need?

A) Configure plan mode as the default for code review sessions.
B) Add the checklist to the project's CLAUDE.md file under a "Code Review" section.
C) Create a /review slash command containing the checklist, invoked when starting reviews.
D) Create a dedicated review subagent with the checklist embedded in its configuration.

Solutions: