LAG over ts partitioned by profile_id is the move here.
Use a window function like LAG to compare each row's visibility with the previous row for the same profile, ordered by timestamp. Then count the number of transitions per profile, sort by count descending and profile_id ascending, and limit to 10.
Pro tip: Clarify the definition of a 'flip'—whether the first observation counts as a transition from NULL—and mention handling ties with a deterministic secondary sort. Also, consider performance implications for large datasets, suggesting partitioning by profile_id.
Identify the columns: profile_id, timestamp, visibility. Define a flip as a change in visibility from the previous timestamp for the same profile. Decide if the first record counts as a flip (usually not).
Use a window function (e.g., LAG) partitioned by profile_id and ordered by timestamp to get the previous visibility value for each row.
Flag rows where visibility differs from the previous visibility (excluding the first row per profile). Then aggregate by profile_id to count the number of flips.
Order the results by flip count descending, then by profile_id ascending to break ties. Limit the output to the top 10 profiles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Took me a second to figure out the right way to get the 'last state' per profile.
First, clarify the schema and definitions: identify the shop table, the state history table with visibility and timestamp, and the given date. Then, for each shop, find the last state at or before the end of the given date, filter for visibility=1, and compute the percentage and raw counts.
Pro tip: Always confirm whether 'currently visible' should consider shops that have no state records at all—these might be excluded or counted as not visible depending on business rules. Also, be mindful of time zones and the exact definition of 'end of day'.
Ask clarifying questions about the tables, columns, and definitions: what is the shop table, what is the state history table, how is visibility recorded, and what is the exact cutoff time (e.g., 23:59:59 in which time zone).
For each shop, find the most recent record at or before the cutoff datetime using a window function like ROW_NUMBER() partitioned by shop_id and ordered by timestamp descending.
From the latest state per shop, filter rows where visibility=1. Count the number of such shops and the total number of shops (or total shops with any state record, depending on definition).
Calculate the percentage as (visible shops / total shops) * 100. Also report the raw counts: number of visible shops and total shops considered.
Check for shops with no state records, null visibilities, or multiple states at the exact cutoff. Decide how to handle these based on business rules and document assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward once you have the current visibility CTE from the previous question.
First, clarify the definition of 'currently visible' (e.g., active status, not deleted, within date range) and the grain of the shops table. Then write a SQL query that filters to visible shops, groups by shop_category, counts the shops, and computes the percentage of total visible shops using a window function or subquery.
Pro tip: Mention that you would validate the results by checking that the sum of counts equals the total visible shops and that percentages sum to 100% (allowing for rounding). Also, consider edge cases like shops with null categories or categories with zero visible shops.
Confirm what 'currently visible' means (e.g., is_visible flag, status, date range) and the expected output format (e.g., category, count, percentage).
Determine the shops table and any necessary joins (e.g., to a status table) and apply the visibility filter.
Use GROUP BY shop_category to count the number of visible shops per category.
Calculate the percentage by dividing each category count by the total visible shop count, using a window function or subquery.
Check that counts sum to total and percentages sum to 100%, and format the output clearly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Break the problem into three parts: data preparation (deduping consecutive states and computing fractions), cohort aggregation (grouping by creation month and calculating count, median, P75, P25), and handling edge cases (incomplete 30-day windows and statistical testing). For the incomplete windows, discuss options like excluding them, using a fixed observation window, or survival analysis, and justify your choice. For statistical testing, propose a non-parametric trend test (e.g., Jonckheere-Terpstra) or regression on cohort-level medians, and mention multiple comparisons if needed.
Pro tip: Emphasize that deduping consecutive identical states is crucial to avoid inflating the fraction due to repeated daily snapshots; also note that using a fixed 30-day window ensures comparability across cohorts, but you must address censoring for recent cohorts.
For each profile, sort daily visibility states by date, remove consecutive duplicates (keeping only state changes), and compute the number of days with final visibility=1 within the first 30 days since creation. The fraction is that count divided by 30 (or by the number of observed days if less than 30).
Group profiles by creation month (cohort). For each cohort, compute the number of profiles, the median fraction, and the 75th and 25th percentiles of the fraction distribution.
For profiles created less than 30 days before the analysis date, decide whether to exclude them, use only the observed days (adjusting the denominator), or apply survival analysis techniques. Document the choice and its impact on comparability.
Test whether the median fraction decreases with newer cohorts using a non-parametric trend test (e.g., Jonckheere-Terpstra) or by regressing cohort-level medians on cohort order. Consider bootstrapping for confidence intervals and adjust for multiple comparisons if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.