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

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

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

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

5. 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 has been operating with 100% human review for 3 months. Analysis shows that extractions with model confidence #90% have 97% accuracy overall. To reduce reviewer workload, you plan to automate high- confidence extractions.
Before deploying, what validation step is most critical?

A) Verify that 97% accuracy meets requirements for all downstream systems that consume the extracted data.
B) Compare accuracy at different confidence thresholds (85%, 90%, 95%) to find the optimal cutoff that maximizes automation while minimizing errors.
C) Analyze accuracy by document type and field to verify high-confidence extractions perform consistently across all segments, not just in aggregate.
D) Run a two-week pilot routing 25% of high-confidence extractions directly to downstream systems and monitor error reports.

Solutions: