Start by clarifying the schema and join keys between the VR usage logs and app catalog, then write a SQL query that filters logs to the last 30 days, aggregates total usage duration per app, and ranks to find the top app. Also discuss data quality checks and potential pitfalls like duplicate logs or missing app mappings.
Pro tip: Mention that you would validate the result by checking for outliers or data anomalies, and consider whether the metric should be total duration or average per user to avoid bias from power users.
Confirm the definition of 'usage duration' (e.g., session length, active time) and the join key between logs and catalog (e.g., app_id). Ask about time zone and whether 'last 30 days' is relative to today or a fixed date.
Use a JOIN between usage logs and app catalog on app_id, filter logs to the last 30 days, GROUP BY app name, SUM duration, ORDER BY total duration DESC, and LIMIT 1.
Check for duplicate log entries, missing app mappings, or null durations. Consider if apps with no usage should be included (they won't appear in logs).
Present the top app and its total duration, and discuss any caveats (e.g., data completeness, whether the metric aligns with business goals).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a window function approach, summing duration per category then dividing by the grand total using SUM() OVER().
First, clarify the definition of 'VR session time' and the app categories of interest. Then, outline the data model and calculation method to compute the percentage of total session time per category, ensuring to handle edge cases like overlapping sessions or missing data.
Pro tip: Emphasize that you would validate the data quality and consider whether session time should be weighted equally across users or if power users skew the results. Mentioning this shows you think beyond the surface-level calculation.
Confirm what constitutes a 'VR session' (e.g., continuous usage without a break) and how app categories are defined. Ensure alignment on the 30-day window and whether it's rolling or fixed.
Locate the relevant tables containing session logs, app usage, and category mappings. Understand the granularity (e.g., per session, per user) and any potential joins needed.
Decide on the formula: sum of session durations per category divided by total session duration across all categories, multiplied by 100. Consider whether to aggregate at user level first or directly sum durations.
Address issues like overlapping sessions, missing category labels, or sessions spanning multiple categories. Decide on imputation or exclusion rules and document assumptions.
Run the calculation, then sanity-check the percentages (sum to 100%) and compare with known benchmarks or trends. Consider segmenting by user demographics or device type for deeper insights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the one that actually required some thought.
Start by defining a clear, quantifiable engagement metric that applies to both social and game apps, such as daily active minutes per user or sessions per week. Then outline the SQL query to compute this metric from event-level data, ensuring proper aggregation and filtering. Finally, describe an appropriate statistical test (e.g., two-sample t-test or Mann-Whitney U test) to compare the metric between the two user groups, considering assumptions and potential confounders.
Pro tip: Acknowledge that engagement is multi-dimensional and propose a primary metric while mentioning secondary metrics for robustness; also highlight the importance of controlling for user tenure and app version to avoid biased comparisons.
Choose a metric that captures user engagement consistently across both app types, such as average daily active minutes per user or number of sessions per user per week. Justify why this metric is meaningful and comparable.
Describe the SQL query to compute the metric: join user and event tables, filter for the relevant time period, aggregate per user, and then group by app category. Mention handling of missing data and time zone considerations.
Propose a statistical test to compare the metric between social and game app users, such as an independent two-sample t-test if assumptions hold, or a non-parametric alternative like Mann-Whitney U. Discuss checking normality and variance homogeneity.
Identify potential confounders (e.g., user demographics, device type, time since install) and suggest ways to control for them, such as stratification, matching, or regression adjustment.
Explain how to interpret the p-value and effect size, and how to communicate findings to stakeholders, including limitations and next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.