# What ML Engineer Interviews ACTUALLY Test

## Метаданные

- **Канал:** StrataScratch
- **YouTube:** https://www.youtube.com/watch?v=2LnZCBCDxws
- **Дата:** 18.05.2026
- **Длительность:** 6:34
- **Просмотры:** 138

## Описание

Most Machine Learning Engineer candidates prepare for model questions.

Then the interview becomes SQL + training data design - and they completely freeze.

In this video, I break down:
✅ What MLE interviewers actually test in data rounds
✅ How to build ML training datasets in SQL
✅ The biggest data leakage mistakes candidates make
✅ How to turn vague business problems into prediction labels
✅ The difference between analyst SQL and ML engineer SQL

Along with real FAANG-style SQL interview questions and a full churn prediction walkthrough.

📚 PRACTICE SQL QUESTIONS USED IN THIS VIDEO
1. https://platform.stratascratch.com/coding/10300-premium-vs-freemium?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
2. https://platform.stratascratch.com/coding/2065-time-from-10th-runner?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
3. https://platform.stratascratch.com/coding/10566-search-click-success-rate-by-user-segment?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
4. https://platform.stratascratch.com/coding/2022-update-call-duration?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
5. https://platform.stratascratch.com/coding/9847-find-the-number-of-workers-by-department?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test

📖 Full article:
https://www.stratascratch.com/blog/how-to-pass-data-interviews-for-machine-learning-engineer-roles?utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test

Interview Preparation:
1. Joins, aggregations, window functions
• https://platform.stratascratch.com/coding/10087-find-all-posts-which-were-reacted-to-with-a-heart?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
• https://platform.stratascratch.com/coding/10558-user-flag-performance-analysis?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
• https://platform.stratascratch.com/coding/9915-highest-cost-orders?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
2. Time-aware SQL: rolling averages, streak detection, session analysis
• https://platform.stratascratch.com/coding/10314-revenue-over-time?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
• https://platform.stratascratch.com/coding/2059-player-with-longest-streak?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
• https://platform.stratascratch.com/coding/2136-customer-tracking?code_type=1&utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
3. Dataset construction & model building:
• https://platform.stratascratch.com/data-projects/prediction-stock-price-direction?utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
• https://platform.stratascratch.com/data-projects/customer-churn-prediction?utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test
• https://platform.stratascratch.com/data-projects/response-marketing-campaign?utm_source=youtube&utm_medium=click&utm_campaign=YT+what+ml+engineer+interviews+test

🔔 Subscribe for weekly ML engineer interview prep and SQL deep-dives.

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📅 Video Timeline:

00:00 Why you froze in your last ML engineer interview
00:25 Why ML interviews are really data interviews (not modeling)
00:48 The 6 things interviewers are secretly testing
01:31 5 SQL skills you MUST have (with practice questions)
02:40 Full worked example: build a 30-day churn prediction dataset
04:38 Validating your dataset in Python (most candidates skip this)
05:00 7 mistakes that get strong candidates rejected
05:28 How interviewers actually grade your answer (3 tiers)
06:00 Your 3-stage prep plan
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📧 Contact Us: Got questions or feedback? Drop them in the comments or email us at team@stratascratch.com.
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#MachineLearningEngineer #MLInterview #DataScienceInterview #SQLInterview #MLEngineerInterview #DataEngineering #MachineLearning #TechInterview #FAANGInterview #LearnSQL #ChurnPrediction #DataScienceCareer #MLOps #StrataScratch

## Содержание

### [0:00](https://www.youtube.com/watch?v=2LnZCBCDxws) Why you froze in your last ML engineer interview

You walk into a machine learning engineer interview thinking it's all about modeling. Then the interviewer asks you to build a training data set in SQL and you freeze. Here's the truth. These interviews aren't about models. They're about data. And most candidates aren't preparing for the right thing. In this video, I'll show you exactly what interviewers test, what trips people up, and how to pass the interview.

### [0:25](https://www.youtube.com/watch?v=2LnZCBCDxws&t=25s) Why ML interviews are really data interviews (not modeling)

Data is everything in machine learning modeling. Join it badly, label it loosely, sneak in leakage, and you've trained a model on garbage. Machine learning engineers spend more time shaping data than tweaking algorithms. Interviewers know this, so they test for it. The one thing they want to see, can you turn messy production data into something a model can actually learn from?

### [0:48](https://www.youtube.com/watch?v=2LnZCBCDxws&t=48s) The 6 things interviewers are secretly testing

Interviewers are testing six things. Briefly, can you define the prediction problem, entity, prediction point, feature window, label window? Do you understand time and leakage, never using future data in your features? Can you reason about table grain before joining? Can you identify meaningful signals instead of dumping every metric in? Can you translate vague business language like predict churn into an exact label definition? And finally, can you explain your trade-offs and validate your own work? If you can do all six out loud before writing code, you're already ahead of most candidates. To get the job, you need to have five

### [1:31](https://www.youtube.com/watch?v=2LnZCBCDxws&t=91s) 5 SQL skills you MUST have (with practice questions)

skills locked in. This is the section where you should pause the video and solve some questions on your own. I'll give you one Strata Scratch interview question for each core SQL skill. All the links are in the description. First skill, multi-table joins. Chain them, but always think about grain first. The Microsoft premium versus freemium question is a good drill. Window functions, row number, rank, dense rank, lag, lead. The EY and Deloitte time from 10th runner question test exactly which ranking function to pick. Cohorts, Microsoft's search click success rate by user segment requires splitting users by registration tenure before calculating anything. Deduplication, Redfin's update call duration is a clean example of using row number to keep only the first row per group. And aggregations, Amazon's workers by department since April question covers the basic pattern you'll repeat across every feature you build. Now the full example. The interview

### [2:40](https://www.youtube.com/watch?v=2LnZCBCDxws&t=160s) Full worked example: build a 30-day churn prediction dataset

prompt is build a data set for predicting whether a user will churn in the next 30 days. Four tables, users, sessions, orders, and support tickets. Before touching SQL, state your assumptions. — One row per user, prediction date January 31st, features from the prior 90 days, label window February 1st through March 2nd. Churn means no session activity in that window. Then explain your approach in one sentence. Past data is features, future data is labels, join everything into one row per user. After that, write the code. Six CTEs, one base users, users who signed up on or before the prediction date. Two, session features, session count, active days, and last session date in the 90-day window. Three, order features, order count, total spend, average order value in the same window. Four, ticket features, support ticket count in the same window. Five, future activity, distinct users with at least one session in the label window. Six labels. Left join base users to future activity. If no match, churn equals one. Final select joins all feature tables and the label to produce one clean row per user. When you put everything together, this is the code you'll end up with. Here's the output. Each feature was chosen for a reason. Sessions 90D is engagement volume, active days 90D is consistency, last session date is recency, orders and spend capture commercial behavior, and tickets 90D captures friction. If you can't explain why a feature is in there, it shouldn't be. Last step, validate. In Python, this is

### [4:38](https://www.youtube.com/watch?v=2LnZCBCDxws&t=278s) Validating your dataset in Python (most candidates skip this)

the code you should write. Row count, label balance, missing values, feature ranges. Even just mentioning this earns you points. Here's the validation output. There are no missing feature values, and the label distribution is balanced. There are four no churn and four churn users. The features have sensible ranges, and nothing looks obviously broken.

### [5:00](https://www.youtube.com/watch?v=2LnZCBCDxws&t=300s) 7 mistakes that get strong candidates rejected

Quickly, the mistakes that kill otherwise strong candidates. Jumping into SQL before defining the problem. Data leakage from using future activity in features. Ignoring row grain and duplicating rows. Answering the analyst version. Outputting a churn rate instead of a training data set. Choosing features without explaining why. Treating the label definition as obvious. Encoding in silence. Always explain what you're doing as you go. Interviewers evaluate your answer in

### [5:28](https://www.youtube.com/watch?v=2LnZCBCDxws&t=328s) How interviewers actually grade your answer (3 tiers)

three tiers. — Full credit means right problem, right shape, logic communicated clearly. SQL doesn't have to be perfect. Partial credit means right direction, but something's missing. — A label definition, a grain issue, unjustified features. Rejected means fundamentally unusable. Wrong output, obvious leakage, or multiple rows per user. They're not grading your syntax. They're grading whether you understood the problem.

### [6:00](https://www.youtube.com/watch?v=2LnZCBCDxws&t=360s) Your 3-stage prep plan

problem. I linked several StrataScratch questions and projects in the description to help you prepare for the interview in three stages. First, build SQL fluency on joins, aggregations, and window functions. Then practice time aware SQL, rolling averages, streak detection, session analysis. Then shift from reporting to dataset construction. That's the stage most people skip. Practice this on machine learning projects. That's it. Drop a comment with what part of machine learning engineer interviews you find hardest. See you in the next one.

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*Источник: https://ekstraktznaniy.ru/video/51756*