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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.
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) Start fresh and re-read the same files
C) Use --resume auth-deep-dive to load that specific session by name
D) Use --session-id with the UUID from yesterday's session transcript file

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 system must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON.
What approach provides the most reliable schema compliance?

A) Define a tool with your target schema as input parameters and have Claude call it with the extracted data.
B) Pre-fill Claude's response with an opening brace to force JSON output, then complete and parse the response.
C) Include detailed JSON formatting instructions and the target schema in your prompt, then parse Claude' s text response as JSON.
D) Append instructions like "Output only valid JSON matching the schema exactly" and implement retry logic to re-prompt when JSON parsing fails.

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.
During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process_refund immediately-but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist.
Which change directly addresses the root cause of the agent fabricating the order_id value?

A) Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
B) Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
C) Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.
D) Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.

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 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) Ask Claude to interview you about the caching requirements before implementing, surfacing considerations like invalidation strategies, cache layers, consistency guarantees, and failure modes.
B) Use plan mode to analyze the current /products endpoint implementation, then provide your caching requirements once Claude explains how the existing code is structured.
C) Write a specification with your known requirements and "TBD" markers for uncertain areas, having Claude propose solutions for each TBD as it implements.
D) 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.

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.
You're implementing the escalation logic for when the agent should call escalate_to_human . Your team proposes four different approaches for triggering escalation.
Which approach will most reliably identify cases that genuinely require human intervention?

A) Instruct the agent to escalate when the customer requests a human, when the issue requires policy exceptions, or when the agent cannot make meaningful progress.
B) Build a rules engine that maps specific issue types, customer segments, and product categories to escalation decisions, removing the need for model judgment calls.
C) Configure the agent to escalate after three consecutive tool calls that fail to resolve the customer's stated issue, ensuring a reasonable attempt before involving a human.
D) Implement sentiment analysis that monitors for frustration indicators (negative language, repeated questions, exclamation marks) and triggers escalation when the frustration score exceeds a configured threshold.

Solutions: