1. the worst post-booking experience
2. increase top-line
3. Pick a music/podcast app
4.the worst post-booking experience on the platform
5.How would you improve it
6. worst post booking experience
7.insert your favorite music app
8.OpenTable's
9.Analyze performance of subscription services
10.Investigate the drop of new customers
11.Investigate the decrease of subscription of dashpass
12.Measure the success of dashpass
13.objective of dashpass
14.Problem of search results
15.Reasons of why no differences in ab tesing
16.Metrics of adding search bar
17.Investigate the increase of dasher waiting time in restaurants
18.Root cause of not working
19.Compare adopters and non-adopters using causal inference
20.Seasonal effect
21.Catering service complain
22.Cooperation with restaurants
23.Percentage calculation
24.Sending coupons
25.Influence of server down
26.Grocery delivery
27.Growth
28.餐馆引入促销功能
29.Increase radius to 10 miles from 5 miles
30.Super late的order
31.Dasher extra pay incentive program
32.超过100刀的订单
33.Grocery store add search bar
34.DashPass
35.Roll out product
36.Data collection
37.SQL filtering and aggregation functions
38.Experiement design
39.Experiement design
40.Experiement design
41.SQL aggregation function
42.Metrics analysis
43.AB Test
44.SQL aggregation function
45.SQL window function
46.SQL aggregation function
47.Ncrease dasher response rate
48.SQL aggregation function
49.SQL aggregation function
50.Metrics evaluation
51.异动分析以及原因
52.AB Test
53.SQL filtering and aggregation functions
54.Explain launched feature only used once
55.Experimental design
56.What metrics will you use to evaluate the impact
57.两个product合并
58.新的城市sign on新的餐厅
59.Maximum Sum of 7 Consecutive Days
60.SQL Add-On Test
61.Business Insights from Sample Deliveries Data
62.SQL Knowledge Assessment
63.Identifying Root Causes for Metric Decline
64.Understanding DoorDash's 3-Sided Marketplace
65.Case Study: Launch Biker
66.SQL Delivery Question
67.Dasher Parking Lot Time Analysis
68.Launching Biker Dasher Case Analysis
69.SQL Month Over Month Sales Percentage Change Calculation
70.Detecting abnormal trends in ETA
71.Choose a key metric for cold food complaints
72.Describe the output of a complex SQL query
73.Machine Learning Depth
74.Incremental Array to Integer Conversion
75.Calculate Percentage of Customers Ordering from Bottom Quantile Restaurants
76.Determine Top Restaurants for Frequent Customers
77.Identify Customers with Highest Number of Deliveries
78.Calculate Infrequent Customer Percentage
79.Fixed Sliding Window Maximum Value Problem
80.Evaluating the Success of the Bike Dasher Launch
81.SQL Query Modification for Different Metrics
82.Data Pipeline and Deployment Details
83.SQL Query - Bottom Quartile Restaurant Orders
84.SQL Query - Month-over-Month Sales Change
85.SQL Query - Most Frequent Customers
86.SQL Query - High-frequency Customers
87.Buddy Strings and K-Anagrams
88.Determining Promotion Discount Rates
89.Designing an A/B Testing Experiment
90.SQL Monthly Data Correction
91.SQL Lag Window Function
92.SQL Reverse Order Query
93.SQL Monthly High Frequency Users
94.Investigating Low Consumer Retention Rate
95.Identify High-Frequency Customers and the Top Customer by Order Count
96.Analyzing Bottom 25% Restaurant Orders and Customers Percentage
97.Month-over-Month Sales Change for a Given Restaurant
98.Identifying Top Customers Excluding High Frequency Ones
99.Calculating High Frequency Customer Percentage
100.eBike Case Study
101.SQL Questions
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