Product and Design Collaboration Questions

Focuses on how design and product teams align, prioritize, and make trade offs to deliver user value and meet business goals. Topics include working with product managers on roadmaps and prioritization, balancing design quality against timelines and scope, advocating for user needs within product constraints, defining success metrics, negotiating trade offs across stakeholders, using prioritization frameworks, and communicating design decisions to product and engineering. Includes examples of pragmatic decision making, cross functional alignment processes, and methods for resolving prioritization conflicts.

HardTechnical

0 practiced

Your model lifts the overall product metric for 95% of users but harms experience for a vulnerable 5% segment. Product wants a global rollout based on the aggregate lift. As a senior ML engineer, propose an ethical and practical plan that includes targeted mitigations, experiments, monitoring, and communication with stakeholders and affected users.

EasyBehavioral

0 practiced

Describe a time when you mediated a prioritization conflict between a product manager and a designer over an ML feature. Explain the situation, how you gathered evidence, what compromise you proposed, and the outcome. Be specific about how you used data and prototypes.

EasyTechnical

0 practiced

You have a transactions table that will be labeled for fraud. Given this schema:

transactions(transaction_id PK, user_id, amount decimal, event_time timestamp, label boolean NULL)

Write a SQL query to return a stratified random sample of up to 100 labeled examples per label class (true/false) and per user cohort (high-activity vs low-activity users). Explain any assumptions you make about cohorts and nulls.

EasyTechnical

0 practiced

You're asked to deliver an image classification feature under a two-week deadline with limited labeled data and a design team expecting high-quality UI behavior. Explain a pragmatic plan you would present to product and design that balances scope, model quality, and timeline. Include proposed MVP behavior, quick data strategies, and how you'll iterate post-launch.

MediumTechnical

0 practiced

Describe a cross-functional checklist and gating criteria you would use with product and design to assess data readiness before greenlighting an ML feature into development. Include minimum dataset size, labeling quality checks, privacy constraints, and instrumentation requirements.

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