First, clarify the schema and definitions: what columns exist in visibility_events, how to identify unique viewers (e.g., user_id), how to compute dwell time (e.g., sum of visible durations), and what 'visible rows only' means (e.g., is_visible = true). Then write a query that filters to the target day and visible events, aggregates per shop to get unique viewer count and average dwell time, applies a HAVING clause for at least 2 unique viewers, and orders by viewer count descending then shop_id ascending.
Pro tip: Mention that you would validate the query on a small sample and check for edge cases like shops with zero visible events or null dwell times, and discuss how you'd handle timezone considerations for 'a single day'.
Ask about the table schema, definitions of unique viewer, dwell time, and visibility. Confirm the date range and timezone for 'a single day'.
Filter rows to the specified day and visible events. Group by shop_id and compute COUNT(DISTINCT viewer_id) and AVG(dwell_time) or SUM(dwell_time)/COUNT(DISTINCT viewer_id) depending on definition.
Use HAVING COUNT(DISTINCT viewer_id) >= 2 to filter shops. Order by unique viewer count DESC, then shop_id ASC.
Check for nulls, duplicates, and performance. Consider indexing on date and shop_id, and test on a sample.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The CASE inside an AVG trick is cleaner than writing two subqueries and I knew that going in, so this one felt okay.
Start by clarifying the schema and definitions: identify the join key between shops and visibility events, define 'visible events' and 'position 3 or above', and confirm the denominator is all visible events per category. Then write a SQL query that joins the tables, filters to visible events, computes the share of events with position <= 3 per category, rounds to 3 decimals, and sorts descending.
Pro tip: Always state your assumptions about the data model (e.g., event grain, join cardinality) before writing SQL—interviewers at Meta care more about your reasoning than perfect syntax. Also, mention that you'd validate the join doesn't duplicate events, as that could skew the share.
Ask about the join key (e.g., shop_id), what constitutes a 'visible event', and how position is recorded. Confirm that 'position 3 or above' means position <= 3 (top of feed).
The share is (number of visible events with position <= 3) / (total number of visible events) per category. Ensure the denominator includes all visible events, not just those in top positions.
Join shops to visibility events on shop_id, filter for visible events, then group by category. Use conditional aggregation (e.g., SUM(CASE WHEN position <= 3 THEN 1 ELSE 0 END) / COUNT(*)) to compute the share, round to 3 decimals, and order by share descending.
Check for edge cases: categories with zero visible events, null positions, or duplicate events from the join. Discuss how the metric could inform product decisions (e.g., feed ranking effectiveness).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
LEAD over a partition by user_id ordered by event_time, then EXTRACT the epoch difference.
Use a window function like LEAD or LAG partitioned by user and ordered by event timestamp to compare each event with the next. Compute the difference in seconds, and ensure the last event per user returns NULL by using LEAD without a default. Filter for visible events and the specific day before applying the window function.
Pro tip: Mention that you would handle ties in timestamps by adding a secondary sort key (e.g., event_id) to ensure deterministic ordering, and clarify that 'visible' events are defined by a flag or condition in the data.
Restrict the dataset to the given day and only visible events (e.g., where is_visible = true). This reduces the data size and ensures the window function operates on the correct subset.
Use PARTITION BY user_id ORDER BY event_timestamp (and a tiebreaker like event_id) to define the sequence of events for each user.
Use LEAD(event_timestamp) OVER (PARTITION BY user_id ORDER BY event_timestamp) to retrieve the timestamp of the next visible event for each row. The last event will have NULL as the next timestamp.
Calculate the difference between the next event timestamp and the current event timestamp, converting to seconds (e.g., using TIMESTAMPDIFF or EXTRACT(EPOCH) depending on SQL dialect). The last event will naturally yield NULL.
Check for users with only one event (gap should be NULL) and ensure the result includes all users. Optionally, discuss how to handle ties or missing timestamps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The SQL part I handled fine after sketching the CASE statement for buckets.
Start by clarifying the definitions and assumptions, especially what 'visible event' and 'dwell >= 5 seconds' mean, and how to bucket account age. Then outline the SQL or pseudocode logic to compute DAU, active users, and active rate per bucket using only today's data, and finally discuss the bias introduced by restricting the denominator to users seen today.
Pro tip: Mention that the active rate denominator should be DAU (users seen today) to match the question, but note that this measures 'engagement among today's users' rather than 'true active rate' across all users. Also, consider edge cases like users with no events today or account age exactly at bucket boundaries.
Define 'visible event' (e.g., event_type = 'visible'), 'dwell >= 5 seconds' (e.g., dwell_time >= 5), and 'account age in days' (e.g., DATEDIFF(today, signup_date)). Confirm that only today's data is used and that DAU is the count of distinct users with any event today.
Create four buckets based on account age in days (e.g., 0-7, 8-30, 31-90, 90+). Ensure buckets are mutually exclusive and cover all possible ages. Use a CASE statement or equivalent to assign each user to a bucket.
For each bucket, count distinct users who had at least one event today (any event, not just visible). This gives DAU per bucket. Ensure you only include users seen today.
For each bucket, count distinct users who had at least one visible event with dwell >= 5 seconds today. This is the numerator for active rate.
Active rate per bucket = active users / DAU per bucket. Then explain the bias: restricting the denominator to users seen today excludes users who were not active today, potentially overestimating engagement and introducing selection bias (e.g., survivorship bias).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
UNION deduplicates across the two sets so a user who both called and received still counts once.
Start by clarifying the schema and the definition of 'distinct users' (caller and receiver IDs). Then write both queries: one using UNION to deduplicate user IDs across the two roles, and one using UNION ALL to concatenate them. Finally, explain the difference in results on sample data and state the general rule for when UNION ALL is safe versus when it double-counts.
Pro tip: Mention that UNION ALL is faster because it skips deduplication, but only safe if you can guarantee no user appears in both caller and receiver roles for the same day—or if you apply DISTINCT after the UNION ALL. This shows you understand performance trade-offs and data semantics.
Confirm the table structure (e.g., call_date, caller_id, receiver_id) and that 'distinct users' means unique user IDs across both roles for the given day.
Use a subquery to select caller_id and receiver_id for the given day, then apply UNION to combine and deduplicate, and finally COUNT(DISTINCT user_id) or COUNT(*) on the deduplicated set.
Use UNION ALL to concatenate caller_id and receiver_id without deduplication, then apply COUNT(DISTINCT user_id) to get the correct distinct count. Alternatively, if you skip the outer DISTINCT, you'll get a double-counted total.
Construct a small sample where a user appears as both caller and receiver on the same day. Show that UNION yields a lower count than UNION ALL without outer DISTINCT, and explain why.
Explain that UNION ALL is safe when the two sets are guaranteed disjoint (no user in both roles) or when you apply DISTINCT afterward. Otherwise, it double-counts users who appear in both roles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Bonus question, they said it was optional but then just waited.
First, clarify the table schema and ensure event_time is sortable. Then, write a SQL query using AVG(dwell_seconds) OVER (PARTITION BY shop ORDER BY event_time ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) to compute the moving average. Finally, explain how the window frame handles edge cases like the first few events.
Pro tip: Mention that the moving average for the first two events per shop will be based on fewer than three events, and consider whether to filter them out or handle them separately. Also, note that if there are ties in event_time, the ordering might be non-deterministic, so adding a tiebreaker like event_id is prudent.
Ask about the table structure: column names, data types, and whether there are multiple events per shop. Confirm that event_time is a timestamp or sortable field.
Specify PARTITION BY shop to compute per shop, ORDER BY event_time to sequence events, and the frame ROWS BETWEEN 2 PRECEDING AND CURRENT ROW to include the current and two preceding events.
Use AVG(dwell_seconds) OVER (PARTITION BY shop ORDER BY event_time ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg. Optionally, include other columns for context.
Discuss how the first two events per shop will have averages based on 1 or 2 events. Consider if you need to filter them out or handle them. Also, check for ties in event_time and add a tiebreaker if necessary.
Describe the resulting column and how it can be used for analysis, such as smoothing out short-term fluctuations in dwell time per shop.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.