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Claude Certified Architect Foundations
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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?
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 must extract event details from calendar invitations and output JSON that strictly conforms to a schema with fields for title, date, time, location, and attendees. Downstream systems reject any malformed or non-conformant JSON. What approach provides the most reliable schema compliance?
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 contacts the agent about a warranty claim on a power drill. Resolving this requires multiple sequential tool calls:get_customerto look up their account,lookup_orderto find the purchase details, and then eitherprocess_refundorescalate_to_humandepending on warranty eligibility. You’re implementing the agentic loop that orchestrates these steps using the Claude API. What is the primary mechanism your application uses to determine whether to continue the loop or stop?
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. An engineer used Claude Code yesterday to investigate authentication flows in a legacy monolith, building up significant context over a 2-hour session. Today she wants to continue that specific investigation. She’s worked on three other codebases since then and knows the session was named “auth-deep-dive”. How should she resume?
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. An engineer used the agent yesterday to analyze a legacy authentication module, identifying two distinct refactoring approaches: extracting a microservice versus refactoring in-place. Today, they want to explore both approaches in depth—having the agent propose specific code changes for each—before deciding which to implement. What’s the most effective way to structure this exploration?
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?
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 callslookup_orderand receives order details showing the item was purchased 45 days ago, how does the agentic loop determine whether to callprocess_refundorescalate_to_humannext?
You are building a structured data extraction system using Claude. The system extractsinformation 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. Testing reveals that when source documents are missing certain specifications, the model fabricates plausible-sounding values to satisfy your schema’s required fields. For example, a document mentioning only dimensions receives a fabricated “weight: 2.3 kg” in the extraction output. What schema design change most effectively addresses this hallucination behavior?
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 a billing dispute resolution, your agent successfully retrieves customer info viaget_customerand order details vialookup_order, but when attempting to callprocess_refund, the tool returns a timeout error. The agent has enough information to explain the charges and verify refund eligibility, but cannot actually process the refund due to the backend failure. What approach best balances first-contact resolution with appropriate error handling?
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 to write a data migration script, but the initial output doesn’t correctly handle records with null values in required fields. What’s the most effective way to iterate toward a working solution?