Metrics question
Design a banking application for a blind customer or with low vision impairment.
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Accessible design thinking, checking whether you can identify concrete barriers for low-vision users in a high-stakes product like banking, not just add generic screen-reader support.
How to approach it
- Define the user: someone with low vision or full blindness managing money independently, who needs both perception and trust in a financial app.
- Walk the current pain points: small text, low contrast, reliance on visual charts, and CAPTCHA or signature steps that assume sight.
- Prioritize core financial tasks first, checking balance, sending money, and detecting fraud, over less critical features like budgeting visualizations.
- Propose accessible-first design: full screen-reader compatibility, audio balance readouts, haptic confirmation for transactions, and voice-guided transfers with confirmation steps.
- Address trust and safety specifically: voice confirmation of transaction amounts before sending money, since a misread number could cause real financial harm.
- Define success as task completion rate and time-on-task for core flows among low-vision users, measured against sighted users as a benchmark.
What a strong answer includes
- Prioritizes core money-moving tasks over cosmetic accessibility fixes, showing the candidate understands what actually matters for this user.
- Names the specific risk of financial harm from misread numbers and designs an explicit voice-confirmation safeguard for it.
- Benchmarks success against sighted-user task completion, a concrete and testable definition of equitable design.
Common mistakes
- Treating accessibility as a checklist of screen-reader labels rather than redesigning core flows around audio and haptic feedback.
- Ignoring the higher stakes of errors in a banking context compared to typical accessibility use cases.
Likely follow-up questions
- How would you test this design with actual low-vision users before launch?
- How would you handle fraud alerts for someone who can't see a visual notification?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop