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Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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.
Your codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today. The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge.
What approach best balances efficiency and accuracy?

A) Start a fresh session to ensure the agent works with current codebase state without stale assumptions
B) Resume the session without informing the agent about the changed files
C) Resume the session and immediately have the agent re-read all 12 previously analyzed files
D) Resume the session and inform the agent which specific files changed for targeted re-analysis

2. 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 used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She's worked on three other codebases since then and knows the session was named "auth-deep-dive".
How should she resume?

A) Use --continue to pick up where the most recent conversation left off
B) Use --session-id with the UUID from yesterday's session transcript file
C) Start fresh and re-read the same files
D) Use --resume auth-deep-dive to load that specific session by name

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 schema includes a skills: string[] field. Production monitoring reveals three consistency issues: (1) compound phrases like "Python and SQL" are sometimes kept as one entry, sometimes split; (2) implied but unstated skills occasionally appear in extractions; (3) similar documents produce wildly different array lengths (5-10 vs 40+ entries). Your prompt currently says "Extract all skills mentioned." What's the most effective improvement?

A) Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.
B) Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.
C) Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.
D) Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."

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) Have Claude extensively research the algorithm and create a detailed implementation plan using extended thinking, then implement the complete solution based on that plan.
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) 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.

5. 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.
When the agent calls lookup_order and receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?

A) The order details are added to the conversation and the model reasons about which action to take.
B) The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
C) The orchestration layer automatically routes to the next tool based on the order's status field.
D) The agent executes the remaining steps in a tool sequence planned at the start of the request.

Solutions: