Original practice question. The scoring rubric is our teaching guide. How we research and score answers
TL;DR: For this 40-minute rehearsal, reserve 5 minutes to clarify and scope, 20 to build, 10 to test and 5 to explain trade-offs. Confirm the actual format and permitted tools with your recruiter; this is a practice plan, not a published employer schedule.
How to approach it
Before building, restate the problem, pick one user and one job, and agree on what a working prototype must demonstrate. Confirm any tool or data restrictions, because employers' rules really do differ. Anthropic's candidate guidance says live interviews are "all you–no AI assistance unless we indicate otherwise", while Canva says it expects engineering candidates to use AI tools in technical interviews (Canva, June 2025). Ask the recruiter which applies to your loop; a prototyping round normally permits the build tools, but do not assume. Explain your choices as you work so the interviewer can follow how you scope, test and revise the product.
A strong answer
The time plan.
| Minutes | Activity | What the interviewer sees |
|---|---|---|
| 0 to 5 | Clarify the user, the job, success criteria; choose a narrow happy path | Product judgment and focus |
| 5 to 25 | Build the core loop with the AI tool; use simple data | Fluency with tools, pragmatic choices |
| 25 to 35 | Write 5 to 10 test cases, run them, record pass or fail | Verification mindset |
| 35 to 40 | Narrate: what works, what fails, what you would do next, risks | Communication and trade-off thinking |
Scoping well. Suppose the prompt is "build something that helps a support team handle refund requests." A narrow happy path: paste a customer message, the tool classifies whether it is a refund request, extracts the order number and reason, and drafts a reply citing the refund policy. Explicitly out of scope: integrations, authentication, real payment actions, multiple languages. Say these out loud.
Building. Use the AI tool to scaffold quickly: a simple page or notebook, a call to a model with a clear prompt, hardcoded sample policy text. Prefer working over pretty. When the tool generates code you do not understand, ask it to explain or simplify, since you may need to debug it live.
Evaluating. Write a small table of test messages: clear refund requests, ambiguous ones, non-refund messages, a message with no order number, an angry message, a message trying to get the bot to promise something outside policy. Run them and mark pass or fail against what you expected. If 6 of 8 pass, explain the two failures and their severity. Eight handpicked cases demonstrate your testing approach; they do not establish production reliability.
Narrating. Summarise: what the prototype does, the evaluation result, the failure modes found, what you would do next (expand the test set, add a human approval step before any refund, handle more languages), and the risks (wrong refund promises, missing order numbers).
Follow the flow, one stage at a time.
Illustrated sequence; animation speed does not represent processing time.Read diagram description
Clarify and scope in the first 5 minutes, build the happy path for 20, evaluate 5 to 10 cases for 10, and narrate trade-offs for the last 5. If time remains, fix one failure and rerun the cases.
Habits that score. Think aloud; say when you are making an assumption; commit to scope rather than chasing every feature; show you know the difference between a demo and something reliable.
What interviewers probe next
"What would it take to ship this?" A larger evaluation set from real tickets, policy checks in code rather than in the prompt, human approval for refunds, monitoring, and handling of languages and edge cases.
"Why did you choose this scope?" Because it lets me test a consequential failure, an unsupported refund promise, within the time. I would check real ticket volumes before claiming this workflow represents most support demand.
How it's scored
| Signal | Weak | Hire | Strong Hire |
|---|---|---|---|
| Scoping | Builds broadly, runs out of time | Picks a reasonable feature | Narrow happy path, stated non-goals, clear success criteria |
| Verification | Shows one example | Tries a few inputs | Small written test set with pass or fail, including adversarial and edge cases |
| Communication | Silent building | Explains at the end | Narrates throughout, names failures honestly and proposes next steps |
Common mistakes
Spending 35 minutes building and none testing.
Polishing the interface instead of checking behaviour.
Building in silence, leaving the interviewer to guess your reasoning.