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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.
Compliance requires that refunds exceeding $500 must automatically escalate to a human agent-this rule cannot be left to model discretion. Despite clear system prompt instructions, production logs show the agent occasionally processes high-value refunds directly (3% failure rate).
How should you achieve guaranteed compliance?

A) Add few-shot examples to the prompt showing correct escalation behavior at various refund amounts ($400, $500, $600).
B) Modify the refund tool to return an error with message "Amount exceeds policy limit-please escalate" when the threshold is exceeded.
C) Strengthen the system prompt with emphatic language: "CRITICAL POLICY: Refunds over $500 MUST trigger human escalation. NEVER process these directly."
D) Implement a hook to intercept tool calls, when the refund process amount exceeds $500, block it and invoke human escalation.

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.
A customer returns 4 hours after their initial session about the same billing dispute. The previous 32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution: 24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information.
What approach most reliably handles returning customers?

A) Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.
B) Resume with full history and configure the agent to automatically re-call all previously used tools at session start to ensure data freshness.
C) Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.
D) Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.

3. 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 infrastructure-as-code repository includes Terraform modules ( /terraform/ ), Kubernetes manifests (
/kubernetes/ ), and CI/CD pipeline scripts ( /pipelines/ ). Each requires different conventions, but your single root CLAUDE.md has grown to 500+ lines. When developers work on Kubernetes files, Terraform-specific rules load into context unnecessarily, consuming tokens.
What is the best approach to reorganize so only relevant guidance loads when editing specific file types?

A) Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*.tf"] ), loading rules only when editing matching files.
B) Restructure the root CLAUDE.md into clearly labeled sections with headers (e.g., "## Terraform Conventions"), improving organization and readability.
C) Split content into subdirectory CLAUDE.md files ( /terraform/CLAUDE.md , /kubernetes/CLAUDE.
md ), so Claude loads directory-specific guidance.
D) Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.

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 schema includes a skills: string[] field. Production monitoring reveals three consistency issues: (1) compound phrases like "Python and SQL" are sometimes kept as one entry, sometimes split; (2) implied but unstated skills occasionally appear in extractions; (3) similar documents produce wildly different array lengths (5-10 vs 40+ entries). Your prompt currently says "Extract all skills mentioned." What's the most effective improvement?

A) Add post-extraction normalization that maps skills to a canonical taxonomy and deduplicates similar entries.
B) Enrich the schema to {skill: string, confidence: float, source_quote: string}[] to capture extraction metadata.
C) Add few-shot examples demonstrating compound phrase handling, explicit mention criteria, and appropriate entry granularity.
D) Add constraints: "Extract 10-20 skills maximum, one skill per entry, only explicitly named skills."

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 system has been running for 3 weeks and human reviewers have corrected 847 extractions. Analysis reveals a recurring pattern: when recipes use informal measurements like "a handful" or "a splash," the model either invents specific amounts or leaves fields empty-accounting for 23% of all corrections.
How should you use this feedback to improve extraction accuracy?

A) Implement a post-processing layer that uses pattern matching to detect informal measurement phrases in source text and automatically populate values when the extraction is empty.
B) Fine-tune the model on the 847 corrected extractions.
C) Update your JSON schema to add a "measurement_type" enum field (precise/informal).
D) Add few-shot examples to your prompt demonstrating correct handling of informal measurements- extracting them verbatim rather than converting or omitting them.

Solutions: