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Interview Questions Guides & Tutorials
Curated interview question banks with answers — SQL, Python, data science, Spark, Power BI, AWS, ADF and more.
Interview Questions articles
System Design Interview Questions for Data Engineers
Data engineering system design interviews reward numbers more than diagrams. This guide gives a framework for the round and works its hardest parts on a real day of New York taxi trips: why sizing for the average builds a six-hour backlog, what Little's law says about delay, and how a natural key overloads partitions.
SQL Query Optimization Interview Questions
Optimisation questions test whether you can explain why a query is slow, not just recite rules. Each answer here is measured on two million real electricity readings in SQLite, with the plan and the work done: indexes, functions on columns, composite and covering indexes, EXISTS, and pagination.
Time Series Interview Questions for Data Science
Time series interviews test whether you respect time: no peeking at the future, honest baselines, and metrics that do not mislead. Seven questions answered on four years of real hourly electricity use: baselines, autocorrelation, what a stationarity test misses, shuffled cross-validation, a leaky feature and MAPE.
Computer Vision Interview Questions and Answers
Computer vision interviews keep returning to one idea: why convolutional networks suit images. Here it is measured on 1,797 real handwritten digits. Shift the test images by one pixel and a dense network falls from 97.8% to 48.3% accuracy, while a convolutional network barely moves, and augmentation closes the rest.
Data Analyst Interview Questions and Answers
Data analyst interviews combine several rounds, each testing a different skill. This guide maps the rounds, gives the questions and the answers interviewers want for each, and works one realistic take-home task on real e-commerce data, where the obvious answer turns out to rest on three sessions.
Data Engineering Interview Questions and Answers
Data engineering interviews test whether you can move data reliably at the right scale. This guide maps the rounds with model answers, then works a design-round sizing task from real New York taxi data, where the numbers show that the obvious big-data design is the wrong one.