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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've asked Claude to write a data migration script, but the initial output doesn't correctly handle records with null values in required fields.
What's the most effective way to iterate toward a working solution?

A) Manually edit the generated code to fix the null handling, then continue working with Claude on other parts.
B) Add "think harder about edge cases" to your prompt and request a complete rewrite of the migration logic.
C) Describe the null value problem in detail and ask Claude to regenerate the entire script with improved edge case handling.
D) Provide a test case with example input containing null values and the expected output, then ask Claude to fix it.

2. 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 new payment processing module that must follow your project's established patterns for database transactions, error handling, and audit logging. You've identified three existing modules that exemplify these patterns: db_utils.py , error_handlers.py , and audit_logger.py . This is a one-off integration task-these patterns are well-documented in your team wiki and don't need additional project-level documentation.
What's the most effective approach?

A) Describe the patterns from the three modules in natural language in your prompt, explaining the transaction handling approach, error format, and logging conventions Claude should follow.
B) Use @ references to include the three modules directly in your prompt, giving Claude concrete code examples of the patterns to follow.
C) Ask Claude to explore your codebase to find and understand the transaction, error handling, and logging patterns before generating the new module.
D) Add documentation of each pattern to your CLAUDE.md file, establishing them as project conventions that Claude will apply automatically.

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) 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.
B) Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.
C) 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.
D) Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.

4. 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 system implements automatic retries when validation fails. On each retry, the specific validation error is appended to the prompt. This retry-with-error-feedback approach resolves most failures within 2-3 attempts.
For which failure pattern would additional retries be LEAST effective?

A) The model extracts dates as ISO 8601 datetime strings ("2023-03-15T00:00:00Z") when the schema requires only the date portion (YYYY-MM-DD).
B) The model extracts keywords as a nested object organized by category when the schema requires a flat array of strings.
C) The model extracts "et al." for co-authors when the full list exists only in an external document not in the input.
D) The model extracts citation counts as locale-formatted strings ("1,234") when the schema requires integers.

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

Solutions: