1.Click through rate and probability of clicks.
2.Title: Addressing Home Feed Staleness Complaints in Focus Groups
3.Cross-Platform Daily User Count and Growth Rate Calculation
4.Thoughts on Threefold Increase in Complaint Volume
5.Exploring the Relationship between Cost per Click and Advertiser Churn: A Deep Dive
6.Measuring and Testing Engagement on Billboard Ads
7.Approaches to Excluding Null Values in Dataframes
8.How long should the experiment be conducted?
9.Output List of Fresh Dates Based on Two Lists
10.User Impression Ratio for Fresh Pins within 7 Days
11.Defining fresh content: metrics and methods
12.What is p-value?
13.Defining a Metric for Homefeed Post Variety
14.Analyzing the Pros and Cons of a Stand-Alone Shopping App for Pinterest.
15.Comparing Freshness of Two Lists of Impression Dates
16.Finding the Percent of Users who Saw Fresh Content.
17.Pin Freshness: Evaluating User Views of New vs Old Content.
18.Evaluating a Novel ML Model for Content Recommendations.
19.Calculating Daily User Engagement
20.Replace Zeros with Column Means and Standardize Matrix Using sklearn
21.Customer Analysis Based on City and Country Fields
22.Return Countries with at Least Two Different Users
23.Selecting rows with missing values in age column of a dataframe
24.Metrics, Treatment Groups, and Experiment Duration: Best Practices
25.Function to Compare Freshness between Two Homefeeds with Dates
26.Calculate percentage of users who viewed fresh pins.
27.Defining and Displaying Fresh Pins.
28.Users by Location: Count, Order, and Total
29.Calculating the Standard Deviation of an Array using NumPy
30.Finding Countries with 2+ Business Users and Unknown Cities
31.Considerations for selecting sample size.
32.Difference between t-test and z-test.
33.Evaluating Freshness Impact & AB Testing Guidelines.
34.Function to Determine the Fresher List of Date Impression
35.Calculating Percent of Users Viewing Fresh Content Daily
36.Filtering Out Null Values in DataFrames
37.Find Fresh Content Viewership Percentage from Two Tables.
38.Pinning Freshness: Gauging User Engagement in Pinterest
39.Evaluating the Impact of a New Algorithm on Fresh Content: Key Considerations and Techniques.
40.Calculating Percentage of Users Seeing Fresh Pins
41.Experimental Design and Metric Setting: A Guide.
42.Boosting Engagement on Homefeed with Fresh Pins: Experimentation and Analysis
43.Fresh Pin Daily Impression Rate among Users
44.What is P-value?
45.Power Analysis: Impact of Sample Size and Element Changes
46.Ad Load Analysis by Country and Day for Non-Spam UsersAnswer e
47.Explaining p value in layman's terms.
48.Designing, Measuring, Randomizing, Timing, and Analyzing Experiments
49.Click Probability and Significance Testing Explanation.
50.Ad Load Analysis by Country and Day in 2020
51.Building and Evaluating a Click-through Prediction Model
52.Dealing with Missing Data: Best Practices
53.Launching Image Search: Metrics, Randomization & Experiment Duration
54.Homefeed monotony: Quantifying and solving user complaints.
55.Matrix Coordinate Neighbor Count
56. connect listeners
57.Time spent on Youtube has gone down by 20% day over day
58.spend less time on the device
59.LeetCode 38
60.Count and Say Sequence Algorithm
61.Employee Free Time Calculation
62.有门二维矩阵最短路径
63.Trial success rate
64.P-value
65.Prob of getting >=5 clicks
66.Hypothesis testing CTR
67.Promoted and non-spam
68.Probability
69.Evaluate model performance
70.Variety level
71.Shopping ap
72.AB testing
73.Freshness
74.Complaint volume up
75.Advertiser churn
76.Freshness of Pins
77.谁先到家
78.Impression table
79.Handle missing values
80.Ads load
81.Impression table
82.相连相等数字
83.Missing value imputation
84.Fresh content
85.Customer table
86.Detecting Soft 404 System Design
87.Top K Elements Coding Problem
88.User Embedding Design
89.ML Pins Graph Problem
90.Merchant Product Information Update Service
91.Type-Ahead Service System Design
92.Escape Room Progress Tracking System
93.Design a system to calculate the sum of a very large dataset
94.Modeling Temporal Features
95.Generating User Embeddings for New Users
96.Sampling Based on Scores Without a Defined Range
97.Describe the process and analyze the results of an experiment that involved changing the content viewed by users.
98.Find all posts violated on a specific date
99.Identify policies violated by a given post id
100.Find all posts violating a specific policy
101.Expression Evaluation with Additional Operations
102.Optimizing Expression Evaluation for Positive Integers
103.Expression Evaluation with Precedence
104.Simple Expression Evaluation
105.Behavioral Interview
106.Coding Problems
107.Machine Learning System Design
108.Design a Machine Learning System
109.Compare Nested Lists
110.Gradient Descent Implementation
111.String Compression Algorithm
112.Calculating Sigmoid Activation Function
113.Regularization Penalties Based on Zeroed Out Coefficients
114.Ensemble Learning Considerations
115.Bootstrap Aggregation Rationale
116.Behavioral Questions
117.Harmful Content Detection Modeling
118.Design Home Feed System
119.Dynamic Programming Problem
120.Reverse a Specific Problem
121.Wait Time Problem
122.Object-Oriented Design for a Modified Linked List
123.Design an Offline Signal Publishing & Serving System
124.Design a Rate Limiter
125.Handling Stream Input for Employee Free Time Problem
126.Minimum Number of Integers to Form Target Sum
127.Algorithm to Ensure Non-Adjacent Elements Are Not the Same
128.Case Study Metrics and Statistical Tests
129.Python/R Data Structure Familiarity
130.SQL Data Assumptions
131.Expression Evaluation with Operator Precedence
132.Evaluate Expression to Target
133.Machine Learning System Design for Similar Pins Recommendation
134.Member and Pin Embedding Generation
135.Monitor Screen Pins Arrangement
136.What is your most proud project?
137.Causal Inference and Test Statistics
138.Python Coding Problem
139.SQL Window Functions and CTEs
140.Cross-Team Collaboration
141.Behavioral Questions
142.Coding Challenge
143.Machine Learning Knowledge
144.Machine Learning System Design
145.Implement a Trie
146.Combination of Operations to Reach Target
147.Count Unique Objects in 2D Array
