Applying Data Science Techniques to Business Problems Questions
Recognizing when A/B testing is appropriate vs observational analysis. Suggesting SQL queries or analysis approaches that would answer the business question. Understanding when you'd need advanced modeling vs simpler analysis. Connecting technical approaches to business decisions (e.g., 'This cohort analysis would tell us whether the decline is from existing users or new users').
EasyTechnical
0 practiced
As an ML engineer, describe a practical checklist you use to decide whether a business question requires a simple analysis (e.g., cohort counts, A/B test) versus building an advanced model (e.g., uplift or causal model). Include data requirements, expected business value, cost-to-implement, interpretability needs, and time-to-live of the decision.
HardTechnical
0 practiced
Short-term experiments show a positive uplift, but you suspect negative long-term effects on retention. Describe experimental designs and analysis approaches to capture long-term causal effects, such as long-horizon holdout groups, staggered rollouts, and panel data methods. Explain how to balance opportunity cost of holdouts with the need for long-term measurement.
MediumTechnical
0 practiced
Given events(user_id, event_date, revenue) and treatment(user_id, assigned boolean, assigned_at), outline an SQL-based approach to estimate incremental revenue attributable to treatment using matching or regression adjustment. Include pre-period baseline construction, matching logic, and assumptions and limitations of your approach.
MediumTechnical
0 practiced
Explain regression adjustment in A/B test analysis. Provide a linear regression specification that estimates the treatment effect while controlling for pre-treatment covariates (for example: outcome ~ treatment + age + prior_activity + device). Describe when adjustment reduces variance and when it could introduce bias.
EasyTechnical
0 practiced
Given an events table: events(event_id, user_id, event_type, campaign_id, occurred_at) where event_type in ('impression','click','purchase'), write a SQL query to compute daily conversion rate per day and campaign for the past 90 days. Show how you would avoid divide-by-zero and handle NULL campaign_id by labeling it 'unknown'.
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