The DATE(request_ts) = DATE(approval_ts) condition is the actual crux here and I almost forgot to scope it that way.
Use a date spine (e.g., generate_series or recursive CTE) to produce the last 7 calendar days, then left join aggregated request and approval counts per day. Compute the acceptance rate as approvals with same-day approval divided by total requests, handling days with zero requests to avoid division by zero.
Pro tip: Always use a date spine to ensure all 7 days appear even if there are no requests; this shows you understand the difference between filtering and joining, and prevents missing days in the output.
Create a list of the last 7 calendar days using a date generation function (e.g., generate_series in PostgreSQL, recursive CTE in other dialects). Ensure the dates are inclusive of today and the previous 6 days.
From the requests table, count the total number of requests for each request date. Group by date to get daily totals.
From the approvals table, count approvals where the approval date equals the request date. Group by date to get daily same-day approval counts.
Left join the daily request counts and same-day approval counts to the date spine. Use COALESCE to replace NULLs with 0 for days with no activity.
Compute the acceptance rate as same-day approvals divided by total requests. Use NULLIF or a CASE statement to avoid division by zero, returning 0 or NULL for days with no requests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward join question but the Users table being incomplete is the gotcha.
Start by clarifying the schema and definitions (e.g., what constitutes a 'friendship request', 'last week', and 'spam flag'). Then write a query that calculates the percentage by dividing the count of non-spam requests by the total requests from last week, using appropriate date filters and joins.
Pro tip: Mention that you would validate the spam flag logic with the trust and safety team, as false positives/negatives could skew the metric. Also, consider if 'last week' should be based on calendar weeks or rolling 7 days, and confirm with stakeholders.
Confirm what 'friendship request' means (e.g., a row in a requests table), how 'spam' is flagged (e.g., a boolean column or a separate table), and the exact time window for 'last week' (e.g., previous calendar week or last 7 days).
Locate the table containing friendship requests (e.g., friend_requests) with columns like request_id, sender_id, receiver_id, created_at, and a way to join to account flags (e.g., accounts.is_spam or a spam_flags table).
Apply a date filter to select only requests created within the defined last week period, using appropriate date functions (e.g., DATE_TRUNC, BETWEEN).
Calculate the percentage as (count of non-spam requests / total requests) * 100, using conditional aggregation (e.g., SUM(CASE WHEN is_spam THEN 0 ELSE 1 END)) or subqueries.
Check for edge cases (e.g., zero total requests) and consider adding a sanity check by also computing the raw counts. Present the final query with clear aliases and comments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Start by acknowledging that incomplete user data can bias metrics, then propose a concrete data or query change (e.g., left join with a fallback dimension table or use of a more complete event log). Follow with a structured plan to validate key hypotheses and edge cases, emphasizing how you would interpret results and communicate uncertainty.
Pro tip: Mention that you would quantify the coverage gap (e.g., % of events with missing user info) and use that to bound the potential bias, showing you think about impact, not just correctness.
Quantify how many users or events are missing from the Users table and identify patterns (e.g., new users, specific platforms, or regions).
Suggest a concrete fix such as joining with a more comprehensive user dimension table, using event-level user attributes, or creating a fallback 'unknown' category with flags.
List key hypotheses about why data is missing (e.g., logging delays, privacy settings, bot traffic) and how they might affect the analysis.
Test edge cases like users with multiple accounts, deleted accounts, or timezone mismatches to ensure the change doesn't introduce new biases.
Assess the impact of the change on key metrics, compare before/after, and clearly communicate remaining limitations and uncertainty to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.