Practice
Workplace simulations and interview practice. Optional beyond the weekday lessons; studios embed selected scenarios. Everything simulated is labelled.
- 100scenarios
- 25capabilities covered
- 62 hof simulated work
- 150interview questions
Scenarios by capability100
Production incident
Senior code review
Stakeholder simulation
Messy real-world data
ADR centre
Live coding and SQL
- SQL gym: rolling and same-weekday features with window functions30 min
- Python gym: entity-safe lag and rolling features in pandas30 min
- SQL gym: consecutive nearly-full streaks and why the first query lies35 min
- Python gym: turning cumulative booking curves into pickup in pandas35 min
- SQL gym: booking pace against the same weekday last year40 min
ML system design
Ambiguous business case
Verbal defense
Statistical traps
Ablations
Decision theory and value
Robustness and stress tests
Paper reproduction
RAG evaluation workbench
LLM failure lab
- Unsupported citations and broken JSON in a claims assistant35 min
- Injected instructions and a looping helpdesk agent40 min
- A claims summariser that never saw its last document35 min
- A refund assistant that paid twice, in the wrong unit35 min
- An oversized voucher late in a long baggage-claim conversation40 min
Multi-agent company
End-to-end lifecycle
Golden test set and regression
Hiring committee
AI cost–quality
Agents vs workflows
Teach-back and Socratic interviewer
Executive presentation
Research reading protocol
- Read a churn paper before believing its abstract35 min
- Read an agent benchmark paper before adopting its framework35 min
- Does long context make retrieval obsolete? Read the claim40 min
- A language-model judge that agrees with humans 85% of the time35 min
- LoRA matches full fine-tuning: how much can 400 invoices show?35 min
Interview gym159
- You have 10 million booking events (customerid, bookedat, flightno). For each booking, flag whether the same customer made another booking within the previous 24 hours.senior15 min
- You are building a training table for a churn model: one row per customer per monthly cutoff, with features such as loyalty tier and number of bookings in the previous 90 days.senior12 min
- A nightly pandas feature job that used to take 20 minutes now takes 3 hours and occasionally runs out of memory. How do you diagnose and fix it?senior12 min
- You have bookings(bookingid, route, bookedat) and a table of fare snapshots faresnapshot(route, snapts, fare) that is appended whenever the price engine publishes a new fare, a few times per day per…senior15 min
- A web-search event log events(customerid, ts) has one row per search. Define a session as a run of events where no gap between consecutive events of the same customer exceeds 30 minutes.senior12 min
- A daily table stores uniquecustomers per route per day. Someone builds weekly and monthly dashboards by summing the daily values, and the weekly numbers disagree with the numbers from the raw events.senior12 min
- An analyst's daily-active-users query runs on events(userid, ts, eventname), a table of about 4 billion rows covering four years and laid out by date of ts (hypothetical sizes): It takes 40 minutes…senior15 min
- Whiteboard case. Design the nightly incremental pipeline that loads booking events from an operational system into an analytics table. Delivery is at-least-once, so duplicates occur;senior30 min