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Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 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.
The system needs to extract candidate information (name, contact details, skills, work experience, education) from uploaded resumes. The extracted data must strictly conform to a predefined JSON schema, as missing required fields or incorrect data types will cause downstream validation failures.
What is the most reliable approach to ensure Claude's output consistently matches the schema?

A. Make two separate API calls--first extracting information as text, then asking Claude to format that text as JSON.
B. Parse Claude's text response with regex patterns to extract JSON objects, using retry logic for malformed responses.
C. Include detailed JSON formatting instructions and a template example in the system prompt, asking Claude to output only valid JSON.
D. Define a tool with an input schema matching your required JSON structure and extract the data from Claude's tool_use response.

Question 2

Your agent has called lookup_order multiple times while investigating a customer's return requests. Each response includes 40+ fields (items, shipping details, payment info, status history). Tool outputs now represent the majority of the conversation's context. The customer mentions two more orders they want to discuss. What's the most effective approach before making additional lookups?

A. Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions
B. Proceed with additional lookups without modifying the existing tool output context
C. Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details
D. Move all tool responses to a vector database with semantic indexing, retrieving relevant portions as the conversation continues

Question 3

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction uses tool use with a JSON schema in which property_type is defined as an enum:
house, apartment, condo, or townhouse. After deployment, 8% of extractions fail schema validation. Investigation reveals that listings mention many uncommon property types--"studio,"
"loft," "duplex," "mobile home," "tiny house," and "converted warehouse"--and new types continue appearing regularly.
What is the most effective long-term solution?

A. Add an other value to the enum with a separate property_type_detail string field for specifics when other is selected.
B. Continuously expand the enum to include newly observed property types and add monitoring for additional edge cases.
C. Change property_type from an enum to a free-form string and implement a normalization step in post-processing.
D. Add few-shot examples demonstrating how to map unexpected property types to the closest existing enum value.

Question 4

The coordinator agent has AgentDefinitions configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice the coordinator correctly reasons about when to delegate - it generates messages like "I'll ask the web search agent to find sources on this topic" - but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors. What is the most likely cause?

A. The coordinator's allowedTools configuration doesn't include "Task", so while it can reason about delegation, it cannot invoke the tool required to spawn subagents.
B. Subagent context isolation means task descriptions from the coordinator don't automatically reach subagents; you need to configure explicit context forwarding in ClaudeAgentOptions.
C. The AgentDefinitions are configured correctly, but the coordinator's system prompt doesn't explicitly list the available subagent types, preventing the model from knowing they can be invoked.
D. The coordinator's max_tokens setting is too low, causing the Task tool invocation to be truncated before the subagent type parameter can be specified.

Question 5

You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction system parses e-commerce product descriptions to extract specifications such as dimensions, weight, and materials into JSON. Despite having a well-defined schema, the model inconsistently extracts the materials field--sometimes returning "cotton blend," other times
"Cotton/Polyester mix," and occasionally omitting the field when material information is clearly present in the source.
What is the most effective way to improve extraction consistency?

A. Switch to a more capable model tier because inconsistent extraction indicates insufficient model capability.
B. Add few-shot examples showing two or three complete input-output pairs with standardized material-description formats.
C. Set the temperature to 0 to eliminate randomness and ensure deterministic outputs.
D. Make the materials field required instead of optional in the schema to force the model to always extract a value.

Solutions: