Practice

159 questions

• Statistics

  A pricing test on one airline route returns p = 0.03 for the difference in conversion between two fare displays.
  mid5 min

• Statistics

  A bank's card-fraud rule catches 95% of fraudulent transactions and flags 2% of legitimate ones. Fraud is 0.5% of the 200,000 transactions processed per day, and the fraud operations team can review…
  mid6 min

• Statistics

  Revenue management asks for "the 95% interval for passengers on flight ZX104 next Friday". A colleague returns the 95% confidence interval of the mean passenger count over the last 52 Fridays.
  senior8 min

• Statistics

  Ancillary attach rate (share of passengers who buy a paid seat or bag) rose in every market segment this quarter, yet the company-wide attach rate fell. The CFO thinks the data are broken.
  senior8 min

• Statistics

  You need a 95% interval for average ancillary revenue per passenger. Most passengers spend nothing and a few spend a lot (group upgrades, excess baggage).
  senior10 min

• Statistics

  Leadership is tired of "not significant" verdicts and asks you to redesign how experiment results are reported so they can answer "how likely is it that this change makes money, and how much could we…
  staff20 min

• SQL and Python

  Using flightday(date, route, passengers, ...), write SQL for a 7-day rolling average of daily passengers per route. Then tell me what breaks if some dates are missing for a route.
  mid10 min

• SQL and Python

  For each route, return the three highest-revenue flight-days. Two flight-days on a route can have identical revenue.
  mid6 min

• SQL and Python

  Two queries in a code review. (1) A report meant to list every flight-day with its web promotion, if any, uses flightday f LEFT JOIN promotions p ON ...
  mid8 min

• SQL and Python

  In pandas you merge a bookings DataFrame (one row per booking) with a customer DataFrame on customerid. The result has more rows than bookings, and some bookings have no customer attributes.
  mid8 min

• SQL and Python

  A colleague computes average load factor per market in pandas with df.groupby("market")["loadfactor"].mean() and reports it as "market load factor". Some rows have a missing market.
  mid8 min

• SQL and Python

  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

• SQL and Python

  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

• SQL and Python

  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

• Analytics

  Monday morning: the weekly bookings dashboard shows bookings down 8% week over week. Your director asks for an explanation by noon. How do you approach it?
  mid10 min

• Analytics

  On a route, load factor rose from 78% to 84% year over year, but revenue fell 3%. Capacity was unchanged. Decompose the change and tell me what you would show the route manager.
  mid8 min

• Analytics

  Product says the new loyalty app is working: "Members who installed the app last year have 30% higher repeat-booking rates than members who did not." How do you evaluate the claim, and what analysis…
  senior12 min

• Analytics

  Finance reports monthly ancillary revenue 3% lower than the product analytics dashboard, and both teams say their number is correct. You are asked to resolve it. How?
  senior12 min

• Analytics

  You must set up automatic alerts on 200 daily KPIs (bookings, revenue, load factor by market and channel).
  senior12 min

• Analytics

  You join an airline's commercial analytics group. There are 400 dashboards, conflicting KPI definitions, and leadership says "we have lots of data but no answers".
  staff25 min

• Classical ML

  What is overfitting? How would you detect it? And in a production forecasting system, how would you tell overfitting apart from temporal leakage?
  mid10 min

• Classical ML

  Your insurance-claims fraud classifier has 99.2% accuracy and 0.93 ROC-AUC. Fraud is 0.8% of claims. The investigation team can review 200 claims a week.
  mid8 min

• Classical ML

  A teammate adds a target-encoded travelagencyid (about 4,000 agencies, many with fewer than ten bookings) to a no-show model. Validation ROC-AUC jumps from 0.71 to 0.86.
  mid10 min

• Classical ML

  In a gradient-boosted model of booking cancellations, SHAP says "days to departure" is the top driver and that higher price lowers cancellation.
  senior12 min

• Classical ML

  A no-show model trained on a dataset where no-shows were undersampled to 50/50 is now used to compute expected no-shows per flight for overbooking.
  senior12 min

• Classical ML

  A bank's small-business credit team uses a logistic-regression scorecard. A new gradient-boosting model improves ROC-AUC from 0.74 to 0.79 on a holdout.
  staff25 min

• Forecasting

  A colleague evaluated a daily passenger forecasting model with shuffled 5-fold cross-validation and reports 3% MAPE. What is wrong, and how would you evaluate it?
  mid8 min

• Forecasting

  A retailer evaluates SKU-store daily demand forecasts with MAPE. Many SKU-days have zero sales. Which metric would you use instead, and why?
  mid8 min

• Forecasting

  You build one global LightGBM model to forecast daily passengers for 300 routes, 14 days ahead. Features include lag1, lag7 and a 7-day rolling mean. Backtest error is excellent.
  senior12 min

• Forecasting

  You are asked to forecast passenger demand for a route to support a decision on adding a second daily flight. Historical passengers are often at capacity on the existing flight.
  senior12 min

• Forecasting

  Airline catering must load meals per flight. Running out of meals costs about 300 per missing meal (compensation, complaints), while an unused meal costs about 100 (hypothetical figures).
  senior12 min

• Forecasting

  In the academy's synthetic airline data, a competitor increases capacity by 25% on three routes from 2024-03-01.
  staff25 min

• DL, NLP and LLMs

  Your neural network's training loss stays flat from the first epoch, and on a second run it becomes NaN after a few hundred steps. Walk me through how you debug it.
  mid10 min

• DL, NLP and LLMs

  Explain self-attention to an engineer who knows RNNs. Why did Transformers largely replace RNNs for language tasks, and what does attention cost?
  mid8 min

• DL, NLP and LLMs

  You must route 50,000 customer emails a month into 12 categories for an airline's service team. You have 8,000 labelled emails. A colleague wants to fine-tune a large language model immediately.
  mid10 min

• DL, NLP and LLMs

  An airline wants an assistant that answers agents' questions about fare rules, baggage policy and rebooking procedures. Policies change monthly.
  senior15 min

• DL, NLP and LLMs

  Explain how LoRA works. For one 4096 x 4096 weight matrix with rank 8, how many trainable parameters does LoRA add, and what are the practical trade-offs compared with full fine-tuning?
  senior12 min

• DL, NLP and LLMs

  Your company is launching LLM-generated summaries of customer-service conversations for supervisors across four languages.
  staff25 min

• RAG and agents

  Walk me through the components of a retrieval-augmented generation (RAG) system, and name one way each component typically fails.
  mid8 min

• RAG and agents

  Users of an internal HR-policy assistant report confident but wrong answers. Product wants to swap in a bigger LLM. How do you find out what is actually wrong before changing anything?
  senior15 min

• RAG and agents

  Your vector search works well for natural-language questions but fails on queries like "fare basis YLOWTR" or "error code E-4012". Why, and what would you change?
  mid8 min

• RAG and agents

  You are building an email assistant agent that reads incoming customer emails, looks up bookings and can send replies and issue travel vouchers.
  senior15 min

• RAG and agents

  A team proposes an autonomous agent to process flight-disruption refund requests end to end. The process has clear eligibility rules but messy inputs (free-text emails, attachments).
  senior12 min

• RAG and agents

  Five product teams are building RAG features on a shared retrieval platform. Documents change daily, teams change prompts weekly, and the model provider updates models a few times a year.
  staff30 min

• Experimentation and causality

  Booking conversion on the website is 5%. You want to detect an absolute lift of 0.5 percentage points (to 5.5%) with a two-sided test at alpha 0.05 and 80% power.
  mid10 min

• Experimentation and causality

  A team ran an A/B test planned for 14 days but checked the p-value every day and stopped on day 6 when it dropped below 0.05. They want to ship.
  mid8 min

• Experimentation and causality

  An experiment configured for a 50/50 split ends with 50,600 users in control and 49,400 in treatment. Treatment shows a significant +2% revenue lift. Do you trust it?
  senior10 min

• Experimentation and causality

  Revenue management introduced a new overbooking policy on 12 routes in March; the other 40 routes kept the old policy. Routes were chosen by managers, not at random.
  senior15 min

• Experimentation and causality

  A team wants to A/B test a new dynamic pricing algorithm by randomising users: half see fares from the new algorithm, half from the old one, on the same flights.
  staff25 min

• Experimentation and causality

  A telecom company sends retention discounts to the 10% of customers with the highest churn-model scores.
  senior12 min

• Experimentation and causality

  A redesigned seat map lifts paid seat selection by 6% in week 1, 3% in week 2 and 2% in week 3. Separately, product complains that tests take too long.
  senior12 min

• Production and system design

  Design a churn-prediction system for a subscription software company with 2 million customers. The retention team contacts at-risk customers weekly.
  senior40 min

• Production and system design

  Design the product recommendation system for an e-commerce site with 20 million monthly users and 2 million products. Recommendations appear on the home page and product pages.
  staff45 min

• Production and system design

  Design a daily demand-forecasting system for a grocery chain: 500 stores, 20,000 SKUs, forecasts 1 to 14 days ahead feeding automatic replenishment.
  senior40 min

• Production and system design

  Design a real-time card-payment fraud detection system for a bank processing a few thousand transactions per second at peak. Decisions must be made inside the authorisation flow.
  staff45 min

• Production and system design

  Design the ranking system for flight search results on a travel site: a user searches a route and date, and you must order hundreds of itineraries.
  staff45 min

• Production and system design

  Before launch, you replay 10,000 recent bookings through the new online no-show scoring service and through the offline batch pipeline used for training.
  mid10 min

• Production and system design

  Describe how you would deploy a new version of a model (or a new prompt for an LLM feature) safely, and how you would decide to roll back. What must be in place before the first deployment?
  senior15 min

• Production and system design

  Your company's LLM features (support assistant, document summarisation, internal search) now cost more than planned, and the CFO wants a 40% reduction in six months without hurting quality.
  staff30 min

• Business judgment

  Before anyone builds it, the head of marketing asks you: "Is a churn model worth it for us?" The company has 1 million subscribers and about 2% monthly churn.
  senior15 min

• Business judgment

  A manufacturing plant uses a model that outputs the probability a part is defective. Inspecting a part costs 20; shipping a defective part costs 500 (warranty and rework).
  mid8 min

• Business judgment

  The chief commercial officer asks: "Should we launch a cheaper basic fare without a checked bag on our domestic routes?" You have 20 minutes. How do you structure your answer?
  senior20 min

• Business judgment

  You lead a team of six. Leadership hands you eight AI/ML project requests for next year (a GenAI customer assistant, demand forecasting upgrade, fraud model refresh, dashboard automation, and four…
  staff25 min

• Business judgment

  Over a year, your team's recommendation model improved click-through rate by 15% in successive A/B tests, but revenue per visitor is flat and returns increased.
  staff20 min

• Communication

  A marketing director expected a new email campaign to lift bookings. Your test result: +0.8% with a 95% confidence interval of -1.5% to +3.1%. How do you communicate this result?
  mid8 min

• Communication

  You spent three weeks analysing why on-time performance fell on regional routes. The COO has five minutes and will read one page.
  senior15 min

• Communication

  Two days before a promised launch, you discover that the demand model's validation used a feature that leaks future information.
  senior12 min

• Communication

  Tell me about a time an analysis or model you delivered turned out to be wrong. What happened and what did you change? (Behavioural: answer using STAR.)
  mid8 min

• Communication

  Tell me about a time you disagreed with a senior stakeholder about a technical or analytical decision and had to influence them without authority. (Behavioural: answer using STAR.)
  senior10 min

• Communication

  Tell me about a time you set technical direction across several teams (for example, a shared evaluation standard, a feature platform, or metric definitions) where nobody reported to you.
  staff15 min

• Statistics

  An airline's baggage team reports (hypothetical figures) 18 mishandled-bag claims on 100,000 bags last quarter and 31 claims on 120,000 bags this quarter.
  mid8 min

• Statistics

  An airline flags the 10% of routes with the worst on-time performance (OTP) in Q1 and sends them a punctuality programme. In Q2 those routes improve from an average of 71% to 77% OTP.
  senior12 min

• Statistics

  An A/B test randomises logged-in users, but the analyst computes the standard error of revenue per session with every session treated as an independent row: 10,000 users, about 5 sessions each, a…
  senior12 min

• SQL and Python

  A dashboard shows the cancellation rate by fare class from a table bookings(bookingid, fareclass, status).
  mid8 min

• SQL and Python

  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

• SQL and Python

  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

• Analytics

  On 30 June an analyst pulls a cohort table of 90-day repeat-booking rate by month of first booking and shows the following hypothetical figures: Jan 17.1%, Feb 16.8%, Mar 17.0%, Apr 15.2%, May 11.1%,…
  mid10 min

• Analytics

  A funnel dashboard reports search-to-purchase conversion at session level (hypothetical figures): mobile 60,000 sessions and 3,000 purchases (5.0%), desktop 40,000 sessions and 3,000 purchases…
  senior12 min

• Classical ML

  A model predicts whether a customer will rebook within 90 days. The table has 10,000 rows from 2,000 customers (five bookings each, on average).
  mid8 min

• Classical ML

  A team tunes a gradient-boosting model with a random search of 200 configurations on one validation set and reports the best validation AUC (0.83) as the expected production performance.
  senior12 min

• Classical ML

  A lender's credit-decision model approves 30,000 of 100,000 applicants (hypothetical) and observes repayment only for approved loans, where 4.0% (1,200) default.
  staff25 min

• Forecasting

  A new daily passenger-demand model has an MAE of 6.0 passengers on the latest validation week, and the dashboard says its error is "71% lower than the naive forecast" (the naive forecast being…
  mid8 min

• Forecasting

  Three teams forecast the same quantity at different levels: network planning forecasts total weekly demand, route managers forecast their own routes, and finance consumes a budget number.
  staff25 min

• Experimentation and causality

  A new in-app 'price alert' feature is rolled out in an A/B test: 50,000 users get access to the feature and 50,000 do not (hypothetical). Only 30% of users with access ever turn the feature on.
  senior12 min

• Experimentation and causality

  An airline launches a lounge-subscription marketing campaign in one city (Izmir) and cannot randomise users. There are 20 comparable cities that did not get it.
  senior15 min

• Experimentation and causality

  Your company runs about 200 A/B tests a year. The product organisation reports that 25 'winning' tests shipped last year, with measured lifts that sum to +25% in the key metric.
  staff25 min

• Business judgment

  A marketing email campaign cost 81,000 and was sent to 450,000 customers, with 50,000 comparable customers held out at random (hypothetical figures).
  senior15 min

• Business judgment

  Nine months into a demand-forecasting rebuild, the team has reached 70% of the accuracy-improvement target. The sponsor says "we have already spent 1.2 million; we must finish".
  staff25 min

• Communication

  The VP of customer experience messages you: "Why are customers so unhappy about baggage? I need this for Thursday's leadership meeting." You have survey data, claims data and a customer-service…
  mid8 min

• Communication

  The executive team must decide this week whether to launch an AI-assisted pricing feature to all markets.
  staff20 min

• DL, NLP and LLMs

  An LLM step that extracts a fare class from customer emails returns different answers when the same email is sent twice. A teammate says "just set temperature to 0 and it is deterministic".
  mid8 min

• DL, NLP and LLMs

  You must fine-tune a transformer classifier, but only 8 sequences of up to 512 tokens fit on one GPU at a time and the recipe you are reproducing used a batch size of 512.
  senior15 min

• DL, NLP and LLMs

  A team fine-tunes an instruction-following model on 20,000 labelled airline customer-service replies. Task accuracy on their evaluation set rises (hypothetical numbers) from 71% to 89%, and they want…
  senior15 min

• DL, NLP and LLMs

  A customer-support routing step calls a large hosted LLM 30 million times a month. An engineer proposes distilling it into a small self-hosted model.
  staff25 min

• RAG and agents

  You are chunking 10,000-token policy documents for a RAG system. A teammate proposes 500-token chunks with a 100-token overlap.
  mid8 min

• RAG and agents

  Your production RAG system uses a 1,024-dimension embedding model over 4 million chunks. The provider announces that the model will be retired in six months and offers a new model with a different…
  senior15 min

• RAG and agents

  An internal agent has 20 tools (booking lookup, fare rules, refunds, vouchers, email send, and so on), each with a short description and a JSON schema.
  senior15 min

• RAG and agents

  A legal-operations team asks for an assistant that answers questions over a set of about 300-page contract bundles. A vendor demo simply pastes the whole bundle into a long-context model.
  staff25 min

• Production and system design

  A regulator asks your team to explain one specific loan decline from nine months ago: what exactly did the model see, which model version produced the score and why. Nobody can reproduce it.
  senior12 min

• Production and system design

  A churn model scores 20,000 customers a week. Churn is defined as no activity for 60 days, so the true outcome for a scored customer is known only two months later.
  staff25 min

• Production and system design

  A customer asks to have their data deleted. You discover it appears in raw logs, a feature table, a training set used by three deployed models, a vector index for an assistant, and the prompts and…
  senior15 min

• Production and system design

  Leadership wants ML-backed services to have service-level objectives like the rest of engineering, but a model can be fast and available while giving worse answers.
  staff25 min

• Classical ML

  You model ancillary revenue per booking with linear regression. Revenue is strongly right-skewed, so you fit it on the log scale and get a good fit.
  mid8 min

• Classical ML

  In a no-show model, the baggage-weight field is missing for 40% of high-risk-segment bookings and 5% of the rest (hypothetical). An analyst wants to drop rows with missing values;
  senior15 min

• Forecasting

  A weekly demand forecast for a route has been reviewed with MAE and it looks acceptable. A planner says "it keeps coming in low".
  mid8 min

• Forecasting

  The airline launches a new route next quarter and the network team needs weekly demand forecasts and a first capacity plan, but the route has no history.
  senior15 min

• Forecasting

  The demand-planning team asks for two months of work to take weekly SKU-store forecast error (WAPE) from 14% to 12%. You are asked to decide whether it is worth it.
  staff25 min

• Statistics

  A weekly count of baggage-delay complaints averages 40 with a variance of 160 across weeks. A colleague builds a control chart with limits at the mean plus or minus three Poisson standard deviations…
  mid8 min

• SQL and Python

  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

• SQL and Python

  Bookings are stored with UTC timestamps. The daily bookings report for the London office groups by the UTC date, and the commercial team says Sunday numbers do not match their own system, especially…
  mid8 min

• Experimentation and causality

  A pricing-page test randomised 40,000 users (20,000 per arm). The analyst compared revenue per session across about 160,000 sessions with a two-sample test and got p = 0.03.
  senior12 minin review

• Experimentation and causality

  Your company's experimentation platform has just been rebuilt. Before trusting it, you are asked to run 100 A/A tests (both arms see the same experience) at a 5% significance level.
  mid8 min

• Experimentation and causality

  Over a year your product organisation ran 300 experiments and shipped the 25 "wins" (significant at 0.05, positive).
  staff25 minin review

• Experimentation and causality

  You randomise 50/50 on whether users are offered a new "trip planner" flow. Only 30% of users in the treatment arm actually open it; control users cannot access it.
  senior12 minin review

• Analytics

  You have events(userid, eventname, eventts) with names 'search', 'select' and 'pay'. Write SQL for the funnel search to select to pay: users reaching each step, step conversion, in the correct order.
  mid10 min

• Analytics

  A monthly cohort table (hypothetical, data through the end of April) shows customers by first-booking month and the share who booked again in month 1, 2 and 3 after joining.
  senior12 minin review

• Analytics

  Your customer dimension is a Type 2 slowly changing table customerscd(customerid, tier, validfrom, validto) with validto exclusive and 9999-12-31 for the current row.
  senior15 minin review

• Analytics

  Marketing adds up the bookings that each channel's own platform report claims: paid search 45,000, social 30,000, email 25,000, affiliate 20,000 (hypothetical monthly figures;
  staff25 min

• Business judgment

  A team proposes a forecasting project. Hypothetical estimates: build cost 250k, run cost 90k per year, benefit of 400k per year if it works, with a ramp to half the benefit in year 1, and a 60%…
  mid10 min

• Business judgment

  Leadership sets the goal "reduce route-level forecast MAPE by 20% this year" and ties bonuses to it. After six months the average MAPE across 200 routes (unweighted) dropped from 21.3% to 16.6%.
  senior15 min

• Business judgment

  Your team of five engineers (about 100 productive engineer-days per month) operates 14 production models, each costing around 4 engineer-days a month in monitoring, retraining and incident work, so…
  staff25 min

• Communication

  A colleague's slide shows on-time performance for last quarter and this quarter (96% and 98%) as a bar chart with the vertical axis starting at 94%. The title is "On-time performance doubled".
  mid8 min

• Communication

  The route-level weekly demand forecast has a MAPE of about 7% (hypothetical). An operations VP wants to use it for next-day staffing at flight level, where flights are around 150 passengers, and says…
  senior12 min

• Communication

  Tell me about a time you decided to stop or significantly de-scope a project that had a lot of sunk cost, and how you brought the people involved with you. (Behavioural: answer using STAR.)
  staff15 min

• Production and system design

  Your weekly batch job scores 2 million customers and appends rows to a scores table consumed by the CRM.
  mid12 min

• Production and system design

  A ranking service must answer within 300 ms at the 99th percentile. For each request it calls five independent backend services in parallel (inventory, price, user profile, ML features, ad filters);
  senior15 min

• Production and system design

  Six teams each own several models, 40 in total, retrained weekly on different stacks. Incidents keep happening because a retrained model is promoted with a regression nobody noticed for days.
  staff40 min

• Statistics

  A league table ranks two regional carriers on on-time performance (OTP), defined as the share of operated flights that arrive within 15 minutes of schedule (hypothetical figures).
  mid10 min

• Statistics

  A small test on a new checkout button gives 12 purchases from 200 control visitors and 20 purchases from 210 treatment visitors (hypothetical).
  senior15 min

• Statistics

  An experiment's primary metric was flat. A stakeholder asks for a breakdown by 40 customer segments and finds four segments with p-values 0.004, 0.019, 0.038 and 0.047 (hypothetical;
  senior12 min

• Classical ML

  You fit a logistic regression for flight cancellations on bootstrap resamples of the same data. Two features, distancekm and scheduledminutes, correlate at 0.98.
  mid10 min

• Classical ML

  Marketing asks for "four customer segments" from a k-means model run on 120,000 loyalty members, using total spend (range 0 to 50,000), number of trips (0 to 30), days since last trip, and share of…
  senior15 min

• Classical ML

  A lender's credit model is retrained yearly on loans it approved. Leadership now wants to raise the approval rate from 40% to 60% of applicants and asks you to certify that the new model is safe.
  staff25 minin review

• DL, NLP and LLMs

  You must classify baggage-damage photos into five damage types with 1,200 labelled images. The photos come from about 400 claims, three photos per claim.
  mid12 min

• DL, NLP and LLMs

  You serve a 7-billion-parameter decoder-only model in 16-bit precision on one 80 GiB GPU for a support assistant with 8,192-token contexts. Throughput is far below the benchmark, and requests queue.
  senior15 min

• DL, NLP and LLMs

  Whiteboard case. An airline's contact centre wants one intent-classification and summarisation capability for customer messages in Turkish, English and a long tail of other languages, replacing…
  staff30 min

• RAG and agents

  For one test question in a RAG system, the retriever returns ten chunks. The chunks that a labeller marked relevant sit at ranks 2, 5 and 9;
  mid10 min

• RAG and agents

  Whiteboard case. Design an internal RAG assistant for a company of 20,000 employees. Documents live in many systems (wiki, tickets, drive, HR) with their own permissions;
  senior35 min

• RAG and agents

  A research agent for an airline's network-planning team calls a search tool, a SQL tool and a spreadsheet tool in a loop, up to eight steps per task.
  senior15 min

• SQL and Python

  Given bookings(customerid, bookedon) with several rows per customer, write a monthly cohort retention table: cohort = month of a customer's first booking, columns = months since the first booking,…
  mid12 min

• SQL and Python

  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

• SQL and Python

  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

• Forecasting

  You build one model to forecast daily passengers 1 to 14 days ahead. A teammate's feature set contains passengerslag7, passengerslag14, holidayflag, competitorfaretoday and weatheractual.
  mid10 minin review

• Forecasting

  Your model's nominal 80% prediction intervals for weekly route demand cover the actual value only 62% of the time across 500 backtest forecast origins. Planners use the upper bound to set capacity.
  senior15 min

• Forecasting

  Whiteboard case. A retailer wants one forecasting platform for 50,000 SKU-store series (daily, 28 days ahead) that feeds replenishment, with category managers entitled to override numbers.
  staff40 minin review

• Production and system design

  Whiteboard case. A payments-risk team and a recommendations team both want "fresh" per-user features, such as transactions in the last hour and average basket in the last 30 days, at serving time…
  senior35 min

• Production and system design

  Whiteboard case. Design a system that chooses which of six ancillary offers (seat, bag, lounge, meal, upgrade, none) to show each customer during booking, 200,000 impressions per day.
  staff40 min

• Production and system design

  A monitoring job reports a Population Stability Index of 0.066 for the feature daystodeparture and 0.317 for bookingchannelshare. The model's score distribution looks unchanged.
  mid10 min

• Business judgment

  A four-week pilot of a pricing-recommendation model on 20% of routes shows revenue +1.1% against control, with a 95% interval of -0.3% to +2.5%.
  senior15 min

• Business judgment

  The CFO asks you to report the annual business value created by the company's ML systems. Five teams each submit a number from their own A/B tests: 6.0, 5.2, 4.1, 3.0 and 2.2 million (hypothetical).
  staff25 minin review

• Statistics

  A satisfaction analysis regresses a passenger's 1-10 satisfaction score on the delay they typed into a post-flight survey ("how many minutes late was your flight?").
  mid10 min

• Classical ML

  A teammate classifies 100 labelled claims as fraudulent or not using 2,000 candidate features extracted from claim text and metadata.
  mid10 min

• Analytics

  The airline's post-flight survey was sent to 50,000 passengers (hypothetical): 10,000 business-cabin and 40,000 economy. Response rates were 30% and 6%, so 3,000 business and 2,400 economy answered.
  senior15 min

• Forecasting

  A marketing analyst finds that the weekly "cheap flights" search-interest index and weekly bookings, over 104 weeks, have a correlation of 0.95 (both series are simulated for this exercise).
  mid10 min

• DL, NLP and LLMs

  You compare two prompts for an LLM that extracts the refund reason from customer messages, on the same 200 labelled messages. Prompt A gets 156 right (78%), prompt B gets 164 right (82%).
  senior15 min

• RAG and agents

  A support assistant answers policy questions with retrieval over the policy library. Policies are re-published under the same title, and the index contains the current version and up to two…
  senior15 min

• Experimentation and causality

  A new rebooking flow should cut support calls and must not increase flight cancellations through accidental self-service cancels. The cancellation rate in control is 2.0% (hypothetical).
  senior15 min

• Business judgment

  A retention programme calls customers ranked by an uplift model. A holdout experiment on 10,000 customers (hypothetical, 1,000 per decile of predicted uplift) shows the incremental number of…
  senior15 min

• Communication

  Operations has decided to redefine the company's on-time performance (OTP) KPI: from "share of operated flights arriving within 15 minutes of schedule, measured at wheels-on" to "share of scheduled…
  staff20 min