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Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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 contracts that frequently include amendments. When a contract contains both original terms and later amendments (e.g., original clause specifies "30-day payment terms" while Amendment 1 changes this to "45 days"), the model inconsistently extracts one value or the other with no indication of which applies.
What's the most effective approach to improve extraction accuracy for documents with amendments?

A) Implement post-extraction validation using pattern matching to detect amendments and flag those extractions for manual review.
B) Preprocess documents with a classifier that identifies and removes superseded sections before the main extraction step.
C) Add prompt instructions to always extract the most recent amendment value and ignore superseded original terms.
D) Redesign the schema so amended fields capture multiple values, each with source location and effective date.

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.
Production logs reveal inconsistent error handling: when lookup_order fails, the agent sometimes retries 5+ times (wasteful when the order ID doesn't exist), sometimes escalates immediately (premature for temporary network issues), and sometimes asks users for clarification (inappropriate when the issue is a backend permission error). Investigation shows your MCP tool returns uniform error responses: {"isError": true,
"content": [{"type": "text", "text": "Operation failed"}]} . The agent cannot distinguish between error types.
What's the most effective improvement?

A) Implement retry logic with exponential backoff in your MCP server for all errors, returning to the agent only after retries are exhausted.
B) Enhance error responses with structured metadata-include error_category (transient/validation
/permission), isRetryable boolean, and a description of what caused the failure.
C) Add few-shot examples to the system prompt demonstrating how to interpret error message patterns and select appropriate responses for each.
D) Create an analyze_error MCP tool the agent calls after any failure to determine the error category and recommended action.

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

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.
Your team frequently migrates React components to Vue. You've written a step-by-step workflow for Claude Code to follow during each migration, and you want every developer on the team to invoke it by typing
/migrate-component . The workflow should stay in sync as the team iterates on it.
Where should you place the skill file?

A) In .claude/skills/migrate-component/SKILL.md at the project root, committed to version control.
B) In ~/.claude/skills/migrate-component/SKILL.md on each developer's machine.
C) As a detailed instruction block in the project's root CLAUDE.md file.
D) In the project's .claude/settings.json using a skillOverrides entry to register and define the workflow.

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.
A customer contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls: get_customer to look up their account, lookup_order to find the purchase details, and then either process_refund or escalate_to_human depending on warranty eligibility. You're implementing the agentic loop that orchestrates these steps using the Claude API.
What is the primary mechanism your application uses to determine whether to continue the loop or stop?

A) You check the stop_reason field in each API response-the loop continues while it equals "tool_use" and exits when it changes to "end_turn" or another terminal value.
B) You manually set the tool_choice parameter to "none" after the final expected tool call to force Claude to stop requesting tools.
C) You track the number of tool calls made and exit the loop once a preconfigured maximum is reached.
D) You check whether Claude's response contains a text content block-if text is present, the agent has produced its final answer and the loop should exit.

Solutions: