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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 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) Update your JSON schema to add a "measurement_type" enum field (precise/informal).
C) Fine-tune the model on the 847 corrected extractions.
D) Add few-shot examples to your prompt demonstrating correct handling of informal measurements- extracting them verbatim rather than converting or omitting them.

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.
Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like
'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order , which catches all exceptions and returns a tool_result with is_error: true and the message
"Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools.
Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?

A) Implement retry logic with exponential backoff inside each tool implementation so transient errors are resolved transparently within the tool before any failure result is surfaced to Claude in the agentic loop.
B) Remove is_error: true and return the error details as normal tool content, so Claude reasons about the response as data rather than treating it as a flagged failure condition that biases retry behavior.
C) Return error-type-specific messages with is_error: true , e.g., "Order not found-try get_customer to search by phone" for data errors and "Database timeout (transient)-retry should succeed" for infrastructure errors.
D) Add an error classification step in the agentic loop that intercepts tool errors before Claude sees them, then routes to hardcoded retry or escalation logic.

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) Keep the root CLAUDE.md and use @path/to/import syntax to modularly include tool-specific guidance files from separate documents.
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) Create files in .claude/rules/ with YAML frontmatter path-scoping (e.g., paths: ["terraform/**/*.tf"] ), loading rules only when editing matching files.

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.
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) 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.
B) 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.
C) 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.
D) Resume with full history and configure the agent to automatically re-call all previously used tools at session start to ensure data freshness.

5. 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 order details are added to the conversation and the model reasons about which action to take.
B) The agent executes the remaining steps in a tool sequence planned at the start of the request.
C) The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
D) The orchestration layer automatically routes to the next tool based on the order's status field.

Solutions: