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Anthropic Claude Certified Architect – Foundations Sample Questions:

1. 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.
During testing, you find that when a customer says "I need a refund for my recent purchase," the agent calls process_refund immediately-but populates the required order_id parameter with a plausible-looking but fabricated value instead of first calling lookup_order to retrieve the actual order ID. The refund call fails because the fabricated ID doesn't exist.
Which change directly addresses the root cause of the agent fabricating the order_id value?

A) Update the process_refund tool description to explicitly state that order_id must be obtained from a prior lookup_order call and must never be assumed or invented.
B) Add server-side validation that checks whether the order_id exists in your database before executing the refund, returning an error to the agent if not found.
C) Pre-parse incoming customer messages to extract any order IDs mentioned, and inject them into the conversation context before passing to Claude.
D) Switch tool_choice from "auto" to "any" to force the agent to make a tool call on every turn.

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

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 system extracts event metadata (date, location, organizer, attendee_count) from news articles using a JSON schema with all nullable fields. During evaluation, you observe the model frequently generates plausible but incorrect values for fields not mentioned in the article-for example, outputting "500" for attendee_count when the source contains no attendance information.
What's the most effective way to reduce these false extractions?

A) Upgrade to a more capable model tier with improved instruction-following to reduce hallucination tendencies.
B) Add prompt instructions to return null for any field where information is not directly stated in the source.
C) Add a post-processing step using a second LLM call to verify each extracted value exists in the source document.
D) Make all schema fields required (non-nullable) with strict validation rules to ensure the model only outputs verifiable data.

4. 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's CLAUDE.md includes a rule: "Use 4-space indentation and always run Prettier formatting." Despite this, code reviews reveal that roughly 30% of files Claude Code generates use inconsistent formatting-sometimes 2-space indentation, sometimes missing trailing commas. Adding emphasis ("IMPORTANT: You MUST use Prettier formatting") reduces violations to about 15%, but doesn't eliminate them.
What is the most effective way to ensure all generated code is consistently formatted?

A) Configure a PostToolUse hook with an Edit|Write matcher that automatically runs Prettier on each file Claude modifies.
B) Split the formatting rules into path-scoped .claude/rules/ files that load when Claude works on matching file types.
C) Extract the formatting rules into a dedicated skill that Claude loads automatically when generating code, with more detailed examples of correct formatting.
D) Add a Stop hook with a prompt-based check that evaluates whether generated code follows formatting standards and prompts Claude to fix violations.

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 extraction system implements automatic retries when validation fails. On each retry, the specific validation error is appended to the prompt. This retry-with-error-feedback approach resolves most failures within 2-3 attempts.
For which failure pattern would additional retries be LEAST effective?

A) The model extracts dates as ISO 8601 datetime strings ("2023-03-15T00:00:00Z") when the schema requires only the date portion (YYYY-MM-DD).
B) The model extracts keywords as a nested object organized by category when the schema requires a flat array of strings.
C) The model extracts citation counts as locale-formatted strings ("1,234") when the schema requires integers.
D) The model extracts "et al." for co-authors when the full list exists only in an external document not in the input.

Solutions: