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Anthropic Claude Certified Architect - Foundations Sample Questions:

Question 1

After investigating a billing dispute over 25+ turns, you've identified that duplicate charges occurred due to a payment gateway timeout triggering retry logic. The required refund ($847) exceeds your $500 authorization limit You need to call escalate_to_human, and the human agent won't have access to your conversation transcript. What context should you pass to enable effective resolution?

A. The complete conversation transcript with all tool results.
B. The customer's original complaint verbatim plus the tool result excerpts showing duplicate transactions.
C. Your diagnosis and the refund amount only.
D. A structured summary: customer ID, root cause, refund amount, and recommended action.

Question 2

Your MCP server implements a check_availability tool that queries an external calendar API.
During testing, you encounter three error conditions: (1) the tool is called with a malformed request missing the required user_email parameter, (2) the calendar API returns a 404 because the specified user doesn't exist in the calendar system, and (3) the calendar API returns a 503 because the service is temporarily unavailable. How should each error be reported according to MCP's error handling design?

A. Report all three as JSON-RPC protocol errors.
B. Report errors 1 and 2 as JSON-RPC protocol errors; report error 3 as a tool result with isError:
true.
C. Report error 1 as a JSON-RPC protocol error; report errors 2 and 3 as tool results with isError:
true.
D. Report all three as tool results with isError: true.

Question 3

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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

A. Have the agent maintain a scratchpad file that records key findings, referencing it for subsequent questions.
B. Implement automatic context clearing every 15 minutes to ensure the agent starts with fresh, uncontaminated context.
C. Switch to a higher-capacity model tier to provide more context window space for accumulated exploration data.
D. Create summaries of all source files before exploration begins, loading only these compressed representations into context.

Question 4

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 CI pipeline performs security-focused code reviews on approximately 50 pull requests daily, currently costing $150 per day using the synchronous API. Reviews are non-blocking--developers merge after tests pass and address findings in follow-up commits. You are evaluating the Message Batches API for its 50% cost reduction.
What factor most determines whether batch processing is appropriate for this use case?

A. Whether review feedback arriving up to 24 hours after pull-request creation remains actionable.
B. Whether reducing per-review latency from 30?0 seconds to near-instant matters for your workflow.
C. Whether your result-processing system can handle reviews arriving in a different order from the order in which they were submitted.
D. Whether you can structure each review as a single request without multi-turn refinement.

Question 5

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.
In production, you observe that simple fact-checking queries, such as "In what year was the Paris Climate Agreement signed?", traverse all four subagents sequentially, consuming more than 40 seconds and significant tokens per query. Complex comparative research benefits from the complete pipeline. Your query distribution is diverse and continues to evolve as users discover new applications.
What is the most effective approach to optimize for varying query complexity?

A. Create a fast path for factual questions that bypasses subagents entirely, routing every other query through the complete pipeline.
B. Implement pattern-based routing that classifies queries as single-fact, comparative, or analytical and maps each category to a predefined subagent combination.
C. Have the coordinator analyze each query and dynamically determine which subagents are required.
D. Train a query-complexity classifier using labeled historical data to predict the optimal subagent combination, retraining it periodically.

Solutions: