Straightforward aggregation but I second-guessed myself on the date filter syntax for a moment.
Start by clarifying the schema and definitions (e.g., what 'app' means, how minutes are recorded, and whether 'past 30 days' includes today). Then write a query that aggregates total minutes per app over the last 30 days, orders by total minutes descending, and limits to the top result. Use a date filter relative to the current date and handle ties appropriately.
Pro tip: Mention that you would validate the result by checking for data completeness and considering time zone alignment, as Meta often deals with global user data. Also, discuss how you might handle ties or multiple apps with the same total minutes.
Ask questions to understand the table structure, definitions of 'app', 'minutes used', and the exact time window (e.g., last 30 days from today). Confirm whether to include partial days and how to handle ties.
Write a subquery or CTE that sums the minutes used for each app, filtering records to the last 30 days using a date condition like `date >= CURRENT_DATE - INTERVAL '30 days'`.
Order the aggregated results by total minutes descending and use `LIMIT 1` to get the top app. If ties are possible, consider using `RANK()` or `DENSE_RANK()` to identify all apps with the maximum total.
Mention potential issues like missing data, time zone differences, or apps with zero usage. Suggest ways to validate the query, such as cross-checking with a different aggregation or sampling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I reached for a window function and I'm glad I did.
Start by clarifying the schema and definitions: what tables contain app usage data, how time spent is measured, and how apps map to categories. Then write a query that filters to the last 30 days, aggregates total time per category, and divides by the overall total to get percentages. Use a window function or subquery to compute the grand total for the percentage calculation.
Pro tip: Mention that you would validate the time-spent metric for edge cases like background usage or multiple sessions, and consider using a CTE for readability and performance. Also, discuss how you'd handle apps with no category or null values.
Ask about the table structure, time-spent definition, and how app categories are assigned. Confirm the date range and whether 'past 30 days' includes today.
Write a subquery or CTE that filters events to the last 30 days, joins to a category mapping table, and sums time spent grouped by category.
Calculate the overall total time using a window function or a cross join with a total subquery, then divide each category's time by the total to get the percentage.
Round percentages to a reasonable number of decimals, order by percentage descending, and ensure the output includes category name and percentage.
Mention how you would test the query, handle null categories, and consider performance optimizations like indexing on date and app_id.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying what 'consistently engaged' means and how to measure it, then propose a robust metric like the coefficient of variation or a consistency score. Design an analysis comparing the distributions of engagement across categories, using statistical tests to account for differences in user base and potential confounders.
Pro tip: Emphasize the importance of defining engagement consistently and considering the user lifecycle; a common pitfall is comparing raw averages without accounting for frequency and recency, which can mislead. Also, mention that you'd validate findings with a holdout or longitudinal analysis to ensure robustness.
Clarify with stakeholders what consistency means: e.g., daily active usage over a period, low variance in session frequency, or a high ratio of active days. Choose a quantifiable metric such as the coefficient of variation of daily engagement or the proportion of days with activity.
Identify relevant engagement events (e.g., posts, likes, comments) for Social and Game users. Ensure data quality, handle missing values, and define the observation window (e.g., 30 days) and user cohort (e.g., new users).
For each user, calculate the chosen consistency metric (e.g., standard deviation of daily engagement, entropy of activity, or streak length). Aggregate these metrics by category to compare distributions.
Use appropriate statistical tests (e.g., Mann-Whitney U, bootstrap) to compare consistency between categories. Control for confounders like user tenure, demographics, and overall activity level via stratification or regression.
Assess practical significance (effect size) and validate findings with a holdout period or sensitivity analysis. Consider segmenting by user subgroups to uncover nuances.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.