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The spam_folder filter tripped me up first.
Start by clearly defining the data pipeline: filter the Emails table to the last 7 days, join to Deliveries while excluding spam_folder rows and selecting the earliest delivered event per email, then compute the 5-minute delivery flag. Finally, aggregate by provider and recipient domain to compute the proportion, explicitly handling NULLs from missing delivery records as undelivered (0).
Pro tip: Explicitly call out the edge cases — bounces followed by a valid delivery, missing delivery records, and spam_folder exclusion — before writing any SQL; interviewers at Microsoft reward candidates who surface ambiguity and state assumptions rather than silently baking them in.
Filter the Emails table to only rows where send_time falls within the last 7 days (e.g., send_time >= CURRENT_TIMESTAMP - INTERVAL '7 days'). Extract the provider and recipient domain from the email address fields at this stage to keep downstream logic clean.
Filter out any rows in the Deliveries table where event_type = 'spam_folder', then for each email_id select the earliest delivered event using MIN(event_time) WHERE event_type = 'delivered'. This correctly handles the bounce-then-deliver scenario by ignoring event ordering and focusing solely on the earliest delivered timestamp.
LEFT JOIN the filtered Emails table to the prepared Deliveries CTE on email_id, so emails with no delivery record produce a NULL delivery time. Compute a binary flag: 1 if (earliest_delivered_time - send_time) <= 5 minutes, else 0 (treating NULL as 0 via COALESCE or a CASE statement).
GROUP BY provider and recipient_domain, then compute the proportion as SUM(delivered_within_5min_flag) / COUNT(*). Optionally include COUNT(*) as a volume column so stakeholders can assess statistical reliability of each group's proportion.
State your assumptions explicitly: 'delivered' is the only qualifying event type, spam_folder rows are excluded before finding the earliest event, missing records are treated as undelivered, and the 5-minute window is inclusive. Mention that you'd validate row counts at each CTE stage to catch unexpected data issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once you've already built the CTEs from part (a).
Break the problem into sequential filtering steps: first identify message IDs where the earliest delivery event is a bounce, then among those find a subsequent 'delivered' event, and finally compute the lag between the original send time and that first delivered event. Use window functions to rank events per message and apply conditional filtering to enforce the ordering constraint. Express the solution in SQL with clear CTEs to keep logic readable and auditable.
Pro tip: Explicitly define 'first observed delivery event' using ROW_NUMBER() or FIRST_VALUE() partitioned by message_id and ordered by event timestamp — interviewers at Microsoft often probe whether candidates handle ties or ambiguous ordering, so stating your tie-breaking assumption upfront signals analytical rigor.
Identify the relevant tables (e.g., messages with send_time, events with message_id, event_type, event_timestamp). Confirm event_type values ('bounce', 'delivered') and whether a single message can have multiple events of the same type.
Use ROW_NUMBER() or RANK() partitioned by message_id and ordered by event_timestamp to label each event chronologically. This lets you reliably identify the 'first' delivery-related event per message.
From the ranked events, keep only message IDs where the row ranked #1 has event_type = 'bounce'. This isolates the cohort of messages that initially bounced.
Within the filtered cohort, identify the earliest 'delivered' event that occurs after the bounce. Join back to the messages table to retrieve the original send_time for lag calculation.
Filter to rows where the first delivered event timestamp minus the original send_time exceeds 5 minutes, then compute the lag in minutes using DATEDIFF or equivalent. Return message_id and the computed lag.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Treat this as a cumulative window analysis problem where you compute a running delivered-within-5-minutes rate for each email provider (Gmail vs. Outlook) restricted to the ms.com recipient domain, updating the cumulative rate after each send date. Then scan chronologically for the first date where Outlook's cumulative rate strictly exceeds Gmail's cumulative rate, and surface that date along with both rates as the justification.
Pro tip: Emphasize that 'cumulative up to and including each send date' means you must use a running sum of delivered and total sends — not a rolling window — so early-day noise is dampened over time; mentioning this distinction signals strong statistical intuition and prevents a common implementation mistake.
Restrict the dataset to rows where the recipient domain is ms.com and the send date falls within the specified 7-day window. Ensure you have columns for send date, email provider (Gmail vs. Outlook), whether the email was delivered within 5 minutes, and a total send count.
Group by send date and provider to compute two daily metrics: the number of emails delivered within 5 minutes and the total emails sent. This gives you a clean daily summary table with one row per (date, provider) combination.
For each provider, sort by send date and apply a cumulative sum over delivered counts and total counts separately, then divide to get the cumulative delivered-within-5-minutes rate at each date. Using cumulative sums (not averages of daily rates) correctly weights days with higher send volumes.
Join the two cumulative rate series on send date and scan chronologically for the earliest date where Outlook's cumulative rate is strictly greater than Gmail's cumulative rate. This is the candidate switch date.
Return the identified date along with both cumulative rates (e.g., Outlook: 94.2%, Gmail: 91.8%) as the justification. Sanity-check by confirming no earlier date satisfies the strict inequality and that sample sizes on that date are large enough to be meaningful.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.