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Claude Certified Architect – Foundations

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Total Questions : 152

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Question # 41

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.

The coordinator agent has AgentDefinition objects configured for all four specialized subagents, each with appropriate descriptions, prompts, and tool restrictions. During testing, you notice that the coordinator correctly reasons about when to delegate—it generates messages such as, “I’ll ask the web-search agent to find sources on this topic”—but no subagent execution ever occurs. The coordinator then proceeds as if the delegation happened and continues with incomplete information. Logs show no errors.

What is the most likely cause?

Options:

A.  

The AgentDefinition objects are configured correctly, but the coordinator’s system prompt does not explicitly list the available subagent types, preventing the model from knowing that they can be invoked.

B.  

Subagent context isolation means task descriptions from the coordinator do not automatically reach subagents; you must configure explicit context forwarding in ClaudeAgentOptions.

C.  

The coordinator’s allowedTools configuration does not include Agent—formerly named Task—so it cannot invoke the tool required to spawn subagents.

D.  

The coordinator’s max_tokens setting is too low, causing the subagent tool invocation to be truncated before the subagent type can be specified.

Discussion 0
Question # 42

You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.

When analyzing complex legal cases that cite multiple precedents, the document-analysis subagent processes each precedent sequentially. A landmark case citing 12 precedents takes more than three minutes to analyze completely.

What is the most effective way to reduce this latency while preserving the coordinator’s ability to monitor and debug the system?

Options:

A.  

Implement a message queue where precedent-analysis tasks are processed asynchronously by a pool of worker agents.

B.  

Enable the document-analysis subagent to spawn its own specialized subagents dynamically when it encounters cases with many citations.

C.  

Have the coordinator spawn parallel document-analysis subagents, each handling a subset of precedents, and then aggregate the results before synthesis.

D.  

Create a recursive agent hierarchy where analysis agents subdivide work among child agents until reaching single-precedent granularity.

Discussion 0
Question # 43

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?

Options:

A.  

Pre-fill Claude’s response with an opening brace to force JSON output, then complete and parse the response.

B.  

Append instructions like “Output only valid JSON matching the schema exactly” and implement retry logic to re-prompt when JSON parsing fails.

C.  

Define a tool with your target schema as input parameters and have Claude call it with the extracted data.

D.  

Include detailed JSON formatting instructions and the target schema in your prompt, then parse Claude’s text response as JSON.

Discussion 0
Question # 44

During testing, when a customer says, “I need a refund for my recent purchase,” the agent immediately invokes process_refund but populates the required order_id parameter with a plausible-looking fabricated value instead of first calling lookup_order. The refund fails because the invented order identifier does not exist. Which change directly addresses the root cause of the fabricated order_id?

Options:

A.  

Update the process_refund tool description to state explicitly that order_id must come from a successful lookup_order result and must never be assumed, inferred, or invented.

B.  

Change tool_choice from auto to any so Claude must call a tool on every turn.

C.  

Add server-side validation that checks whether order_id exists before attempting the refund and returns an error when it does not.

D.  

Preprocess customer messages to extract any mentioned order identifiers and inject them into the conversation before sending the request to Claude.

Discussion 0
Question # 45

You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.

Your automated review jobs take 18 seconds to initialize before Claude begins analyzing code. Profiling reveals that the delay results from automatically discovering hooks, MCP servers, plugins, skills, and multiple nested CLAUD

E.  

md files throughout the monorepo.

You need to reduce startup time while ensuring reviews still enforce the coding standards documented in the root-level CLAUD

E.  

md file.

What is the most effective approach?

Options:

A.  

Replace the default prompt using --system-prompt-file ./CLAUD

E.  

md, which bypasses default prompt assembly and loads only the project rules.

B.  

Run in --bare mode and pass --append-system-prompt-file ./CLAUD

E.  

md to load the required project standards explicitly while skipping automatic discovery.

C.  

Run in --bare mode and repeat all review criteria directly in the -p prompt for every invocation.

D.  

Keep the default initialization and add --exclude-dynamic-system-prompt-sections to improve prompt-cache reuse across CI runners.

Discussion 0
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