Data Interview Questions

132,938 data interview questions shared by candidates

PLEASE DON'T TAKE THE PHONE SCREENING LIGHTLY! I did and got rejected. I was expecting SQL questions and in general talk about my resume but she asked me a question on product sense and I was completely unprepared for it. Creation of Facebook user groups has gone down by 20%, what will you do? sounds simple but I messed it up so badly. I was just blabbering anything in an unstructured way, I sounded so stupid and not even fit for a small company forget Facebook. The recruiter was nice and she did not say anything but I were to hear my own answer, I would reject myself on spot. I regret it so much wish I could have prepared for it. I hope someone sees this and it helps them. The SQL questions were easy and I did answer them correctly- what kind of joins to get only common rows, what the natural sorting order etc.
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Data Scientist

Interviewed at Meta

3.5
Mar 9, 2020

PLEASE DON'T TAKE THE PHONE SCREENING LIGHTLY! I did and got rejected. I was expecting SQL questions and in general talk about my resume but she asked me a question on product sense and I was completely unprepared for it. Creation of Facebook user groups has gone down by 20%, what will you do? sounds simple but I messed it up so badly. I was just blabbering anything in an unstructured way, I sounded so stupid and not even fit for a small company forget Facebook. The recruiter was nice and she did not say anything but I were to hear my own answer, I would reject myself on spot. I regret it so much wish I could have prepared for it. I hope someone sees this and it helps them. The SQL questions were easy and I did answer them correctly- what kind of joins to get only common rows, what the natural sorting order etc.

SQL - I was given 4 tables Sales, Product, Promotion and something else (Never had to use it). 1. Which product had the highest sales with promotions and sales ( basically a where clause on 2 flags) 2. What % of sales happened on the first and last day of promotion. I don't remember the rest.. I'd say your ability to translate regular english statements into SQL is tested very well
avatar

Data Engineer

Interviewed at Meta

3.5
Apr 3, 2020

SQL - I was given 4 tables Sales, Product, Promotion and something else (Never had to use it). 1. Which product had the highest sales with promotions and sales ( basically a where clause on 2 flags) 2. What % of sales happened on the first and last day of promotion. I don't remember the rest.. I'd say your ability to translate regular english statements into SQL is tested very well

We have two types of reviewers: careful reviewer (80% of reviewers) and lazy reviewers (20% of reviewers). Careful reviewers rate a post positive 60% of time and negative 40% of time). Lazy reviewers however rate a post positive 100% of time. A) what is the probability that a random ad is reviewed positively? B) If an ad gets a negative review, what is the probability that it's reviewed by a lazy reviewer? C) If 3 ads are reviewed positively in a row, what is the probability that they are reviewed by a lazy reviewer? D) Some as above with n positively reviewed ads in a row. What happens when n goes to infinity? E) If we have very few labeled data, how can we build a model to distinguish between careful and lazy reviewers?
avatar

Data Scientist, Analytics

Interviewed at Meta

3.5
Mar 6, 2019

We have two types of reviewers: careful reviewer (80% of reviewers) and lazy reviewers (20% of reviewers). Careful reviewers rate a post positive 60% of time and negative 40% of time). Lazy reviewers however rate a post positive 100% of time. A) what is the probability that a random ad is reviewed positively? B) If an ad gets a negative review, what is the probability that it's reviewed by a lazy reviewer? C) If 3 ads are reviewed positively in a row, what is the probability that they are reviewed by a lazy reviewer? D) Some as above with n positively reviewed ads in a row. What happens when n goes to infinity? E) If we have very few labeled data, how can we build a model to distinguish between careful and lazy reviewers?

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