Prompt
Build a query for week-one activation by signup channel. You have users, events, teams, and purchases. Talk through the query before writing it.
Answer shape
- State the output grain: one row per signup channel and signup week.
- Define activation as a specific event within seven days of signup.
- Pre-aggregate events before joining to users so one user cannot inflate the count.
- Name trust checks: null channel, bot accounts, timezone, duplicate users, and late events.
Run the 15-minute version.
- Minutes 0-3: say the grain, denominator, activation event, date window, and excluded users before writing SQL.
- Minutes 4-9: write the plain version first: users by signup week and channel, left joined to one activation row per user.
- Minutes 10-12: add sanity checks for duplicate events, null channel, trial/test users, timezone boundary, and late-arriving events.
- Minutes 13-15: give the business read: which channel is strongest, what could be wrong, and what you would inspect before recommending a change.
What a strong answer sounds like
"I want one row per signup week and channel. My denominator is users who signed up in that week. My numerator is users with at least one activation event within seven days. I will pre-aggregate events to user level before the join so heavy users do not inflate activation."
Common miss
Do not start by joining every event to every user and counting rows. That can turn usage intensity into fake activation. Interviewers listen for this because it reveals whether you can protect a metric under pressure.
Move from syntax to judgment.
The Product Analytics packet trains SQL follow-ups that turn a correct query into a useful recommendation.
Healthcare or insurance SQL?
If the screen mentions claims, providers, billed amount, paid amount, or rejection rates, run the healthcare claims SQL rep before this generic grain rep.
Direct purchase note
This is the public $59 self-guided packet path. If a coaching or mock-interview session already gave you access, use that access instead of buying the same packet again.
Need a 24-hour prep decision?
Run the self-check to choose the next rep from the real risk in your loop: SQL/OA, product case, metric debugging, or leadership story.
Public checkout is for self-guided packet buyers.
If a coaching, mock-interview, or private session already gave you packet access, use that private access and do not buy the same packet again. The public $59 checkout is for candidates buying the self-guided Product Analytics or Leadership packet directly.
Use Product Analytics for SQL, metrics, experiments, and product cases. Use Leadership for conflict, failure, ambiguity, influence, and final-round stories.
Run one timed rep before checkout.
Pick the risk you can fix today. Do the rep, then buy the packet only if it matches the round in front of you.
- SQL or OA: say the row grain first, solve one baseline query, then name the edge case that could break it.
- Product case: start with the decision, then give the metric view, segment, risk, and recommendation.
- Leadership loop: choose one conflict, failure, or ambiguity story and name the operating change you owned.
Last-mile check: pick the packet for the round you could lose.
Use the rep on this page first. If the weak spot is SQL, metrics, experiments, or product cases, get Product Analytics. If the weak spot is conflict, ambiguity, or final-round stories, get Leadership. Public checkout is $59 and separate from any private session access.