Start by clarifying the data schema and definitions (e.g., what constitutes a session, how duration is calculated, and how apps are identified). Then outline a SQL or pandas approach that filters to yesterday's data, groups by app, and computes the average session duration, handling edge cases like nulls or outliers. Finally, discuss how to validate and interpret the results in a product context.
Pro tip: Mention that you would check for data completeness and potential timezone issues, as 'yesterday' can be ambiguous in global products like Meta's, and ensure that session duration is computed correctly (e.g., end_time - start_time) rather than using a pre-aggregated field that might be inaccurate.
Ask clarifying questions about what defines a session, how duration is measured, and which tables or logs contain the data. Confirm the timezone and date range for 'yesterday'.
Describe how you would filter the data to yesterday's sessions, handle missing or invalid durations, and ensure each session is attributed to the correct app.
Explain the aggregation: group by app and calculate the average session duration (e.g., using AVG(duration) in SQL or groupby.mean() in pandas). Mention whether to use mean or median and why.
Discuss sanity checks (e.g., compare with historical trends, check for outliers) and how you would present the results to stakeholders, including any caveats.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product context and business goal for each app, then select a metric that aligns with that goal (e.g., engagement, retention, or monetization). Calculate the metric from the session data, compare across apps, and justify the best performer by linking the metric to user value and business impact.
Pro tip: Always tie your metric to a specific business objective and acknowledge trade-offs (e.g., a high-engagement app might have lower monetization). This shows you think like a product data scientist, not just a number cruncher.
Ask clarifying questions about each app's purpose, target users, and business model to determine what 'performance' means. For example, a social app might prioritize engagement, while a utility app might prioritize retention.
Choose a metric that directly measures success toward the goal, such as DAU/MAU, average session duration, retention rate, or conversion rate. Define it precisely, including the formula and any assumptions.
Walk through the calculation using the available session data, ensuring you handle edge cases like missing data or outliers. Show the math or describe the aggregation steps clearly.
Compare the metric across apps, considering statistical significance and practical significance. Justify which app performs best by linking the metric to user value and business outcomes, and acknowledge any limitations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the definition of bounce rate as the proportion of sessions where a user switches to another app and then returns to the original app within the same day. Outline a data processing approach using event logs to identify app switches and returns, then compute the rate per app per day. Discuss scalability and edge cases.
Pro tip: Define a session timeout (e.g., 30 minutes) to distinguish separate sessions and avoid overcounting returns from long breaks. Also, consider that bounce rate might be more meaningful when segmented by user cohorts or app categories.
Define bounce rate precisely: a user switches from app A to app B and then returns to app A within the same day. Specify whether it's per session or per user, and how to handle multiple switches.
Assume event logs with user_id, timestamp, and app_id. Sort events by user and time, and identify app switches and returns. Handle missing data and ensure timestamps are accurate.
For each user and day, iterate through their app usage sequence. Count instances where a switch to another app is followed by a return to the original app. Compute bounce rate as (number of such returns) / (total number of app sessions or switches) per app per day.
Discuss distributed processing (e.g., MapReduce, Spark) for large-scale data. Use window functions or stateful stream processing to efficiently detect patterns.
Consider edge cases: multiple returns, same app switch, time zone handling, and session boundaries. Validate with sample data and discuss potential biases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business question and defining 'ad creation source' and 'revenue' precisely. Then outline a structured SQL query that joins ad creation data with revenue data, aggregates daily revenue per source, and filters for the past month. Finally, discuss how to present the results effectively for stakeholders.
Pro tip: Mention the importance of handling time zones and date boundaries consistently, and consider whether 'daily revenue' should be attributed to the ad creation date or the revenue date—this nuance shows deep understanding of data modeling.
Ask clarifying questions to define 'ad creation source', 'revenue', and 'past month' (e.g., last 30 days vs. calendar month). Confirm the granularity and any filters like ad status or region.
Locate the relevant tables: one for ad creation (with source and creation date) and one for revenue (with ad ID, date, and revenue amount). Ensure you understand the join keys and relationships.
Write a SQL query that joins the tables, filters for the past month, groups by ad creation source and date, and sums revenue. Use appropriate date functions and handle time zones if necessary.
Check for data quality issues (e.g., missing sources, negative revenue) and validate results with sanity checks. Interpret the output to identify trends or anomalies.
Summarize findings in a clear table or visualization, highlighting key takeaways such as top-performing sources or daily fluctuations. Suggest next steps if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Needed to define 'least active' first, which I took as lowest total spend.
Clarify the definition of 'least-active' (e.g., fewest ads, lowest spend, or lowest impressions) and the time window, then write a SQL query that aggregates ad activity per advertiser, orders ascending, limits to 10, and joins to a country dimension. Discuss how you would handle ties, missing data, and whether to consider only active advertisers or all advertisers.
Pro tip: Mention that 'least-active' could be misleading if you don't filter out advertisers with zero ads or test accounts; also consider that low activity might be due to seasonality or new advertisers, so segment by advertiser tenure or category to provide actionable insights.
Ask the interviewer to define 'least-active' (e.g., by ad count, spend, impressions) and specify the time period and whether to include all advertisers or only those with some activity. Confirm the expected output format.
Locate the ads data table (e.g., ad_events, ads) and the advertiser dimension table with country information. Determine the join keys (e.g., advertiser_id) and any filters needed (e.g., date range).
Write a SQL query that groups by advertiser_id, counts ads (or sums spend/impressions), orders ascending, and limits to 10. Use a subquery or CTE to first aggregate then join to country.
Consider ties (use RANK or DENSE_RANK), null countries, and whether to exclude advertisers with zero activity. Validate results by checking counts and ensuring no duplicate advertisers.
List the top 10 advertisers with their countries and activity metric. Briefly interpret: are these new advertisers, seasonal, or from specific regions? Suggest next steps like deeper segmentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the definitions of 'ad creation source' and 'advertisers spending above 1000' (e.g., currency, time period, spend threshold). Then, write a SQL query that aggregates spend per advertiser per source per year, filters for spend > 1000, counts distinct advertisers, and computes the ratio (current year / previous year) for each source.
Pro tip: Mention that you would handle edge cases like division by zero (e.g., no advertisers above 1000 last year) by using NULLIF or COALESCE, and consider whether to use advertiser-level or account-level spend.
Confirm the definition of 'ad creation source' (e.g., API, Ads Manager, etc.), the currency and time period for spend, and whether 'this year' and 'last year' refer to calendar years or rolling 12 months.
Write a subquery that sums ad spend for each advertiser, ad creation source, and year, ensuring you filter for the relevant years.
From the aggregated data, filter for advertisers with total spend > 1000 in each year, then count distinct advertisers per source per year.
Join the counts for this year and last year per source, and calculate the ratio (this_year_count / last_year_count), handling division by zero.
Sanity-check results (e.g., ratios > 1 indicate growth), and consider if any sources have insufficient data or outliers that might skew the ratio.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one felt more like a stats or experimentation question dressed up as SQL.
Start by clarifying that causation requires ruling out alternative explanations, especially cannibalization. Propose a framework that combines experimental design (e.g., holdout tests) with observational causal inference methods (e.g., difference-in-differences, instrumental variables) to isolate the effect of the new ad source from concurrent changes in other sources.
Pro tip: Emphasize the importance of defining a clear counterfactual: what would have happened to other sources if the new source had not been introduced? This shows you think like a scientist, not just a correlator.
Clearly state the hypothesis: the new ad source causes a decrease in other sources (cannibalization) versus independent growth. Define metrics for each source and total revenue.
If possible, run a randomized controlled trial (e.g., geo-based holdout) where the new source is introduced in some regions but not others. If not, use quasi-experimental methods like difference-in-differences or synthetic control.
Compare changes in other sources between treatment and control groups. Look for negative correlations or offsetting effects. Use regression models to estimate the causal impact of the new source on each other source.
Check if the revenue increase from the new source is offset by decreases elsewhere. Conduct sensitivity analyses to rule out confounding factors (e.g., seasonality, external events).
Summarize whether the evidence supports cannibalization or independent growth. Discuss limitations and suggest next steps for validation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.