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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's exploration subagent spent 30 minutes analyzing a legacy payment system, reading 47 files and documenting data flows. The session was interrupted when the engineer's connection dropped. While away, a teammate merged a PR that renamed two utility functions. The engineer wants to continue the same exploration.
What's the most effective approach?

A) Launch a fresh subagent with a summary of prior findings.
B) Resume the subagent from its previous transcript and inform it about the renamed functions.
C) Launch a fresh subagent and include the prior transcript in the initial prompt for context.
D) Resume the subagent from its previous transcript without mentioning the changes-the architecture understanding remains valid.

2. 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 a billing dispute resolution, your agent successfully retrieves customer info via get_customer and order details via lookup_order , but when attempting to call process_refund , the tool returns a timeout error.
The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure.
What approach best balances first-contact resolution with appropriate error handling?

A) Implement automatic retries with exponential backoff for process_refund , keeping the conversation open until the refund is successfully processed.
B) Escalate immediately to a human agent since the refund action cannot be completed.
C) Confirm the refund will be processed and close the conversation, since the system has all necessary information to complete it automatically.
D) Explain the billing, confirm refund eligibility, acknowledge the system issue preventing immediate processing, and offer escalation or retry later.

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.
After expanding the agent's MCP tools with delivery-specific capabilities (check_delivery_status, contact_driver, issue_credit, apply_promo_code, update_delivery_address, reschedule_delivery), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to
71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools-calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data.
Which approach structurally eliminates the semantic overlap identified in the logs as the error source?

A) Add few-shot examples to the system prompt demonstrating correct selection for each ambiguous tool pair, such as showing when issue_credit applies versus when process_refund is appropriate.
B) Split the tools across two sub-agents-a "financial resolution" agent with process_refund, issue_credit, and apply_promo_code, and a "delivery operations" agent with the remaining delivery tools-with a coordinator routing between them.
C) Enable the tool search tool with defer_loading on the six new tools, keeping the original four always loaded, so the agent dynamically discovers specialized tools only when needed.
D) Consolidate semantically overlapping tools-merge issue_credit and process_refund into a single resolve_compensation tool with an action parameter, and fold check_delivery_status into lookup_order with an optional include_tracking flag.

4. 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.
Your process_refund tool returns two types of errors: technical errors ("503 Service Unavailable",
"Connection timeout") that are transient (~5% of calls), and business errors ("Order exceeds 30-day return window", "Item already refunded") that are permanent (~12% of calls). Monitoring shows the agent wastes 3-
4 turns retrying business errors that can never succeed. Currently, both error types return only a plain text message to Claude.
What's the most effective way to reduce wasted retries while improving customer-facing response quality?

A) Add few-shot examples showing how to distinguish retriable from non-retriable errors by parsing error message text.
B) Implement automatic retry logic at the tool layer for technical errors only, passing business errors to Claude without retries.
C) Add a check_refund_eligibility tool that must be called before process_refund to prevent business rule violations.
D) Return structured error responses with "retriable": false for business errors and a customer-friendly explanation for Claude to use.

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

Solutions: