1.Metrics for a New Product Release
2.SQL Execution Order and Its Importance
3.Metrics for Search Functionality Issues
4.SQL Query with Aggregation and Window Functions
5.Minimum Collaboration Effort in a Graph
6.Triple Entertainment Value Maximization
7.Identifying and Analyzing New Types of Bad Content
8.Metrics to Measure Video Content Quality
9.SQL Query Execution with Time Constraints
10.Handling Specific Work-related Problems
11.Data Processing Model for Real-Time Analytics
12.Data Processing Model for Real-Time Analytics
13.Choosing Metrics Based on a Scenario
14.Database Schema Design and SQL Implementation
15.Python Coding Question
16.Counting Unique Viral Content Combinations
17.Optimal Strategy for Watching Reels
18.Calculate the average tenure and spending amount for the first spending across advertiser IDs
19.Calculate the average tenure across advertiser channel
20.Calculate the minimum total cost with vouchers
21.TikTok AI Model Performance Boost
22.Maximize Efficiency with TikTok
23.TikTok Secondary Influencers
24.Explain database isolation levels and transactions.
25.Evaluating the Effectiveness of Game Ads
26.SQL Query for Identifying Users Who Submitted Records in Consecutive Weeks
27.Resume Deep Dive
28.Impact of Feature Normalization on Linear Regression Performance
29.Design a recommendation system for products similar to Amazon's 'customers who bought this item also bought' feature.
30.Explain machine learning basics and discuss the advantages and disadvantages of method X compared to method Y.
31.Conversion Related Issues
32.Diversity in Dataset
33.Selection Bias in Machine Learning
34.Model Optimization for Product Enhancement
35.Designing and Managing Databases
36.Spark Load Distribution
37.City Cluster Analysis
38.Analysis of DAU Decline
39.Designing Metrics for Experiments
40.Chi-Squared Test for Categorical Variables
41.Assumptions and Remedies for Difference-in-Differences (Diff-in-Diff)
42.SQL Window Function Usage
43.Analyzing a Decline in Daily Active Users (DAU)
44.Experimental Design
45.Minimum Sample Size Determination
46.Differences between T-test and Z-test
47.Optimizing Review Count for TikTok Shop
48.AB Testing for TikTok Improvement
49.Project Deep Dive
50.SQL and A/B Testing Knowledge
51.AB Testing Case Study
52.SQL Query for Consecutive User Logins
53.A/B Testing and Statistics
54.Resume Deep Dive on Machine Learning
55.Machine Learning Knowledge Assessment
56.SQL Rolling and Accumulative Sum
57.SQL Ranking
58.Technical Details Discussion Based on Resume
59.ML Design Interview Question
60.Research Deep Dive
61.Identify fraud transactions.
62.Measure the contribution of e-commerce product review quantity to revenue.
63.Standard SQL problems.
64.Logistic Regression Implementation
65.Maximizing Profit by Investing in Products
66.Content Creator Engagement Challenge
67.Social Connections Profile Visibility
68.Write a Linear Regression Algorithm
69.SQL Coding Question
70.How would you help new users or products quickly integrate into the system?
71.ML Ops: Model Re-training Indicators
72.Experience with Recommendation Algorithms
73.Python Space Complexity Challenge
74.SQL Query with CTE and Window Functions
75.Spark Join Use Case
76.Most Challenging Project
1. Metrics for a New Product Release
Apple, one of the clients, has released a new phone and wants to pull some metrics. Which metrics would be important to them and why do they need this data?
2. SQL Execution Order and Its Importance
Discuss the execution order of SQL and why this order is important.
3. Metrics for Search Functionality Issues
Some customers have recently found the search function to be ineffective, such as when searching for themselves (e.g., Apple) on the platform, irrelevant or harmful content appears. What metrics would you consider to evaluate this issue, and how would you approach solving this problem?
4. SQL Query with Aggregation and Window Functions
Given two tables, one view table containing each person's daily views on posts and one post table containing all posts with hashtags, write SQL queries to perform certain operations using aggregation and window functions. One of the operations involves using the LIKE operator to handle text with hashtags.
5. Minimum Collaboration Effort in a Graph
Given an undirected graph with weighted edges, starting node 1 and a given end node, define the effort of a path as the maximum edge weight on that path. What is the minimum effort required to reach the end node from the start? Return -1 if no path exists. Note that the adjacent matrix is not given initially and must be constructed from the provided arrays.
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