This part felt manageable but I second-guessed myself on the 'mutually exclusive' constraint.
Start by acknowledging that 'existing' vs 'new' and 'active' are ambiguous and need clear, mutually exclusive definitions. Propose two definitions for each, discuss their trade-offs, and then select one pair that best balances business relevance and analytical rigor for the given context.
Pro tip: Tie your chosen definitions to a specific business decision or metric (e.g., retention or engagement) to show you understand the 'why' behind the analysis, not just the 'how'.
Acknowledge that 'existing' vs 'new' and 'active' are not universally defined and depend on business context. State that you will propose two reasonable, mutually exclusive definitions for each.
Propose two definitions: (1) based on account creation date relative to the 28-day window (e.g., new = created within window, existing = created before), and (2) based on first activity date (e.g., new = first activity within window, existing = first activity before). Discuss pros and cons of each.
Propose two definitions: (1) any user with at least one session or event in the 28-day window, and (2) users meeting a threshold of engagement (e.g., at least 3 sessions or 10 actions). Discuss pros and cons of each.
Compare the definitions, considering factors like business goals, data availability, and potential biases. Choose one pair that is most defensible and aligns with the analysis objective, explaining your rationale.
Briefly mention how you would translate the chosen definitions into SQL (e.g., using date filters and aggregation) and what metrics you would compute (e.g., active rate per group).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and definitions (e.g., what constitutes an active user, how to label new vs. existing). Then outline a step-by-step SQL approach using CTEs and window functions to compute rolling metrics, ensuring proper handling of partial observation windows and avoiding double-counting. Finally, discuss how to validate results and handle edge cases.
Pro tip: Explicitly state your assumptions about the data (e.g., daily grain, event timestamps) and how you'd handle users with no events—this shows you think about data quality and edge cases, which is crucial for product analytics at Meta.
Identify the tables, columns, and grain of the data. Define what 'active' means (e.g., at least one event on a day) and how to label users as 'new' (signed up within the last 28 days) or 'existing'.
Aggregate events per user per day to get daily active users and event counts. Use a CTE to avoid double-counting if multiple events occur.
For each date and cohort, compute 7-day rolling active rate and average daily events per user over the last 28 days. Use window functions like AVG() OVER (PARTITION BY cohort ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) for 7-day, and similar for 28-day.
For users who signed up within the window, adjust denominators to only include days they were observed. This may require calculating the number of days each user was active or observed and using that in the denominator.
Join and aggregate to produce the final table with date, cohort, active_users, total_users_observed, active_rate_7d, and avg_events_per_user. Validate by checking for double-counting and ensuring rates are between 0 and 1.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew this was a Simpson's paradox setup the second they said 'control for country mix.' Wrote the country-level breakdown first, then did a weighted average using the existing cohort's country distribution as the reference weights.
First, extend the query to include country-level breakdowns for both new and existing cohorts, then compute weighted averages using each country's share as weights. To control for country mix differences, use the existing cohort's country distribution as the standard and apply it to the new cohort's country-level metrics. This standardization ensures a fair comparison by removing confounding from differing country compositions.
Pro tip: When presenting, clarify that you're using direct standardization with the existing cohort as the reference population, and mention that this is equivalent to a weighted average where weights are the reference country shares. Also, check for countries with small sample sizes and consider pooling or excluding them to avoid unstable estimates.
Modify the query to group results by country for both new and existing cohorts, ensuring you capture the metric of interest (e.g., conversion rate) and sample sizes per country.
Calculate the metric for each country within each cohort. For rates, compute the numerator and denominator per country to allow proper weighting later.
Use the existing cohort's country distribution as the standard weights. Compute each country's share of the total existing cohort population (or relevant denominator).
For the new cohort, multiply each country's metric by its corresponding weight from the existing cohort, then sum these products to get the standardized weighted average. Optionally, do the same for the existing cohort to verify it matches the overall existing metric.
Check that the weighted average for the existing cohort equals its overall metric (if weights are based on it). Compare the standardized new cohort metric to the existing cohort metric to assess the true difference, controlling for country mix.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Easy to talk through, harder to sound like you're not just reciting a list.
Name two concrete bias risks relevant to the analysis (e.g., selection bias and survivorship bias), then describe a specific SQL-side mitigation you implemented (e.g., using a LEFT JOIN with COALESCE to handle missing data). Keep the answer focused on the query you wrote, not general theory.
Pro tip: Choose biases that are directly tied to the data pipeline or query logic, and explicitly state how your SQL mitigation reduced the bias—this shows you understand both the statistical concept and its practical implementation.
Briefly state the analysis you performed (e.g., A/B test on user engagement) to ground the biases in a concrete scenario.
Select two biases that are plausible given the data and query, such as selection bias (e.g., only including users who logged in) and survivorship bias (e.g., analyzing only users who completed the experiment).
Describe one specific SQL technique you used to mitigate one of the biases, such as using a LEFT JOIN to include all users, filtering with WHERE clauses to avoid conditioning on post-treatment variables, or using window functions to handle missing data.
Explain how the SQL mitigation directly addresses the bias risk, e.g., 'By using a LEFT JOIN, I ensured that users with no events were still included, reducing selection bias.'
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.