1.A/B Testing and Statistics
2.Resume Deep Dive on Machine Learning
3.Machine Learning Knowledge Assessment
4.SQL Rolling and Accumulative Sum
5.SQL Ranking
6.Technical Details Discussion Based on Resume
7.ML Design Interview Question
8.Research Deep Dive
9.Identify fraud transactions.
10.Measure the contribution of e-commerce product review quantity to revenue.
11.Standard SQL problems.
12.Logistic Regression Implementation
13.Maximizing Profit by Investing in Products
14.Content Creator Engagement Challenge
15.Social Connections Profile Visibility
16.Write a Linear Regression Algorithm
17.SQL Coding Question
18.How would you help new users or products quickly integrate into the system?
19.ML Ops: Model Re-training Indicators
20.Experience with Recommendation Algorithms
21.Python Space Complexity Challenge
22.SQL Query with CTE and Window Functions
23.Spark Join Use Case
24.Most Challenging Project
1. A/B Testing and Statistics
Explain how you would design and analyze an A/B test. Discuss the statistical methods you would use.
2. Resume Deep Dive on Machine Learning
Discuss in detail the machine learning aspects of your resume.
3. Machine Learning Knowledge Assessment
The interviewer did not specify a particular question but indicated a desire to assess knowledge in machine learning. Since the candidate chose to discuss machine learning, the interviewer likely expected a conversation around core ML concepts, algorithms, and possibly some practical applications.
4. SQL Rolling and Accumulative Sum
Write an SQL query that calculates rolling sum and accumulative sum for a given dataset.
5. SQL Ranking
Write an SQL query to rank users according to their scores. Explain the differences between RANK, DENSE_RANK, and ROW_NUMBER functions in SQL.
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