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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.
Monitoring shows 12% of extractions fail Pydantic validation with specific errors like "expected float for quantity, got '2 to 3'". Retrying these requests without modification produces identical failures.
What's the most effective approach to recover from these validation failures?

A) Implement a secondary pipeline using a larger model tier to reprocess documents that fail validation.
B) Set temperature to 0 to eliminate output variability and ensure consistent formatting.
C) Send a follow-up request including the validation error, asking the model to correct its output.
D) Pre-process source documents to standardize problematic formats before sending them for extraction.

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.
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) 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.
D) 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.

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 used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth-having the agent propose specific code changes for each-before deciding which to implement.
What's the most effective way to structure this exploration?

A) Resume yesterday's session and explore both approaches sequentially within the same conversation thread.
B) Use fork_session to create two branches from yesterday's analysis, exploring one approach in each fork.
C) Start two fresh sessions, manually providing a summary of yesterday's analysis findings to establish context.
D) Resume yesterday's session to explore the first approach, then start a new session for the second, manually recreating the original context.

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 pipeline processes restaurant menus and must output structured JSON with fields for item names, descriptions, prices, and dietary tags. Some menus use inconsistent formatting-prices as "$12" vs
"12.00", dietary info as icons vs text.
What's the most reliable approach?

A) Request multiple extraction attempts per document and select the most common format.
B) Use separate extraction calls for each field to ensure consistent handling of each type.
C) Extract data as-is and normalize formats in post-processing code after Claude returns.
D) Define a strict output schema and include format normalization rules in your prompt.

5. 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.
Your codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today. The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge.
What approach best balances efficiency and accuracy?

A) Resume the session and immediately have the agent re-read all 12 previously analyzed files
B) Resume the session without informing the agent about the changed files
C) Start a fresh session to ensure the agent works with current codebase state without stale assumptions
D) Resume the session and inform the agent which specific files changed for targeted re-analysis

Solutions: