Answer their real
interview questions
out loud
Zynter builds its sets around the rounds real loops actually run — then makes you say your answers to a voice interviewer that follows up until something gives. Almost nobody freezes on the code. They freeze on the follow-up.
No scheduling. No subscription. First session ready in under a minute.
“Your query works. Now the table is 400 million rows. What breaks first?”
“Slower how? Give me the mechanism, not the symptom.”
“It stops using the index and falls back to a sequential scan — so I’d partition by day.”
- Meta
- Amazon
- Apple
- Uber
- DoorDash
- Discord
Voice, a real editor, and a follow-up you didn’t want.
Nothing here is a mock-up. This is the room you sit in — the interviewer talking, your code running, the clock going.
Built per company, per level, per round.
Every set is tagged by company, experience level and format — so you rehearse the shape of the interview in front of you, not a generic problem list.
Examples of the rounds these sets run
- MetaCompute 7-day rolling retention from a raw events table.SQL · Senior Data Engineer · two follow-ups on scaleHard
- AmazonTell me about a time you shipped something you knew was wrong.Behavioural · SDE II · pushed for the metric, every timeMedium
- UberDesign the notification fan-out for 40 million users.System design · Senior · duplicate-vs-drop is the real questionHard
- DoorDashModel a multi-venue ordering system with inventory holds.Data modelling · Senior · concurrency on the same itemMedium
- AppleGiven a stream of prices you cannot re-read, return the max profit.Python · SDE II · complexity defended out loudEasy
Every round in the loop, not just the coding one.
Run one round to fix a weak spot, or the whole loop back to back the night before.
Python
Real execution against test cases, in a full editor. Complexity defended out loud.
SQL
Real schemas with sample rows. Graded on correctness and on how you got there.
Data modelling
Schema design from requirements, then normalisation trade-offs under questioning.
System design
HLD and LLD, pushed with follow-ups until something in your design gives.
Behavioural
STAR practice that pushes for specifics and numbers — whichever letter you skipped.
The scorecard a real interviewer writes about you.
Same shape as a hiring loop’s debrief: a rubric, a score per question, and the exact line in the transcript where the points went. You keep the code you wrote and every hint you took.
- Correctness9/10
- Communication7/10
- Query efficiency6/10
- Depth on follow-up4/10
- 14:22What breaks first at 400 million rows?
- 14:31Um… I guess it just gets slower?
- 14:34Slower how? Give me the mechanism.
Where it went — you had this. The words you needed were “sequential scan”. Name the mechanism, then the fix, and this question is an 8 rather than a 3.
Cheap enough to do it badly the first time.
One bad mock teaches you more than ten clean solves alone — but only if a bad one does not cost a hundred dollars and a week of scheduling.
~$4/ 30 min
Credits burn only while you are inside a session. A full 60-minute mock lands around $8.
- Every format and every company set
- Full transcript, code and scorecard kept
- No subscription, nothing expiring monthly
~$100/ hour
A human mock-interview service, booked days ahead.
- Scheduled, not on demand
- Feedback quality depends on who you get
- No transcript, no score, no history
What changes after a few sessions.
Turn interview anxiety into deliberate practice — and track the change across every session.
“I could not afford $100 per session. I ran multiple Python + SQL sessions every week, and the per-question feedback made it obvious what to fix next.”
From zero mocks to 3 offers in 6 weeks
Backend Engineer
“The AI interviewer kept pushing me for specifics and numbers. Seeing transcripts side-by-side showed exactly how my stories improved over time.”
Behavioral answers finally clicked
New Grad
“Running queries, seeing failures, and getting explanations after each attempt made me way more confident for real take-home tasks.”
SQL stopped being guesswork
Data Engineer
Preparing a whole cohort?
Zynter is multi-tenant underneath — your space, your question bank, your numbers.
- Your own subdomain
- A branded space at your-name.zynter.ai, with your own sign-in.
- Your own sets
- Author question banks in-house and release them per cohort.
- Cohorts and groups
- Group students, then assign the loops that match their targets.
- Admin analytics
- See where the cohort is weak, and who is actually ready.
Before you start.
Walk in having already answered it.
Run their loop tonight, at your desk, for about the price of lunch.
No scheduling · No subscription · Ready in 60 seconds