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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.
The system routes documents with extraction confidence below 85% to human review. A quarterly audit reveals that 12% of high-confidence extractions (#85%) also contain errors-cases where the model finds plausible-but-incorrect values. Error sources vary: comparison tables showing competitor specs, appendices referencing different product variants, and ambiguous phrasing the model misinterprets. You need a sustainable strategy to catch these high-confidence errors and measure whether improvements reduce the error rate over time.
What approach is most effective?

A) Implement stratified random sampling reviewing a fixed percentage of high-confidence extractions weekly, enabling error rate measurement and novel pattern detection.
B) Add a verification pass that re-extracts from each high-confidence document, flagging cases where the two extraction attempts produce different results.
C) Lower the confidence threshold from 85% to 70%, routing a larger volume of extractions to human review.
D) Implement heuristic rules that flag documents containing comparison tables or appendices for review regardless of confidence score.

2. 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.
You need to add a date validation check ensuring event dates are in the future. This requires adding a conditional statement to one existing function in a single file.
What is the most appropriate approach?

A) Use direct execution to make the change.
B) Enter plan mode first to create a detailed implementation strategy before making the change.
C) Start with extended thinking mode enabled to ensure thorough reasoning about the validation logic.
D) Enter plan mode to analyze how the validation might impact other parts of the reservation flow.

3. 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 agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
C) The order details are added to the conversation and the model reasons about which action to take.
D) The orchestration layer automatically routes to the next tool based on the order's status field.

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.
The agent verifies customer identity through a multi-step process before resetting passwords. During testing, you notice that after the customer answers the third verification question, the agent asks them to provide their name again, as if the earlier exchange never happened.
What's the most likely cause of this behavior?

A) The prompt lacks instructions telling Claude to remember information across multiple exchanges.
B) Claude's memory retention is limited to two conversational turns by default, requiring explicit configuration to extend it.
C) The verification tool is clearing the agent's internal state after each successful validation step.
D) The conversation history isn't being passed in subsequent API requests.

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.
You've asked Claude Code to build a PDF report generation feature. The initial implementation queries the database correctly, but the output has formatting issues: table columns are too narrow causing content truncation, dates display without proper formatting, and page break handling is incorrect. You've noticed these issues interact-changing column widths affects how dates render, and page breaks depend on content height.
What's the most effective approach for iterating toward a working solution?

A) Show Claude an example of a correctly formatted report and ask it to match that output, rather than listing the specific technical issues.
B) Provide all three issues in a single detailed message with exact specifications for each, allowing Claude to address them together in one update.
C) Start fresh with a detailed prompt specifying all formatting requirements upfront.
D) Address the column width issue first with specific measurements, verify it works, then fix date formatting within the corrected columns, then adjust page breaks-testing after each change.

Solutions: