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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 invoices and extracts line items, subtotals, tax amounts, and grand totals.
During evaluation, you discover that in 18% of extractions, the sum of extracted line item amounts doesn't match the extracted grand total-sometimes due to OCR errors in the source document, sometimes due to extraction mistakes by the model. Downstream accounting systems reject records with mismatched totals.
What's the most effective approach to improve extraction reliability?

A) Extract line items and totals independently, then use a separate validation model to reconcile discrepancies by determining which extracted values are most likely correct.
B) Add few-shot examples demonstrating invoices where extracted line items sum correctly to the stated total, encouraging the model to produce mathematically consistent extractions.
C) Implement post-processing that automatically adjusts line item amounts proportionally when their sum doesn't match the stated total.
D) Add a "calculated_total" field where the model sums extracted line items alongside a "stated_total" field. Flag records for human review when values differ.

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

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

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

5. 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 has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response ) in addition to tools.
How do these MCP prompts become accessible within Claude Code?

A) They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session.
B) They appear as slash commands (e.g., /mcp__servername__deploy_checklist ) that you can invoke, with arguments passed after the command name.
C) They are surfaced as @ -mentionable resources alongside files, fetched and attached to your message when referenced.
D) They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task.

Solutions: