Interview Questions

Yenenash W. - "p- value --> Assuming the null hypothesis is true, probability of observing the data as extreme as the observed data. Example - You're running an experiment on a new checkout flow. Control converts at 10.0%, treatment converts at 10.8%. You run a two-sample z-test and get p = 0.03. What this means: If there were truly no difference between control and treatment (null hypothesis), there's only a 3% chance you'd see a difference of 0.8pp or larger just from random sampling noise."See full answer

Steve Y. - "Define: How is daily post view calculated Isolate Issues: Data issue Time period Geo IOS vs Android vs Web Correlated Metrics in the funnel DAU Time spent/ scrolls Engagement - likes, comments External factors Competitor actions Big events Internal factors Product launch Feature change"See full answer

Vips M. - "No discussion around better initialization of weights like Xavier etc.?"See full answer

Rafał P. - "def changeString(org: str,target:str) -> bool: lOrg = len(org) lTarget = len(target) \# They have to be equal in lenght if lOrg != lTarget: return False counter1 = Counter(org) counter2 = Counter(target) \# Counter internally iterates through the input sequence, counts the number of times a given object occurs, and stores objects as keys and the counts as values. if counter1 != counter2: return False diff = sum(org[i] != target[i] for i in range(n)) return diff == 2 or (diff == 0 and any(v > 1 f"See full answer

Satesh A. - "I dont know is good and you should use however than but."See full answer

Jerry O. - "\# Definition for a binary tree node. class TreeNode: def init(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def maxPathSum(self, root: TreeNode) -> int: self.max_sum = float('-inf')"See full answer

Richard W. - "int getMaxWater(vector& nums) { int n = nums.size(); int mx = INT_MIN; int i=0, j=n-1; while(i<j) { int water = (j - i) * min(nums[i], nums[j]); mx = max(mx, water); if(nums[i] < nums[j]){ i++; } else { j--; } } return mx; } `"See full answer

Frank A. - "1) create the experimental and control groups. 2) Then calculate the proportion (mean) of the true conversion rates for both groups using the convert column which counts True as 1 and False as 0. This is their conversion rates 3) calculate the statistic of the two groups by subtracting the proportion and standardizing. 4) get the p-value and compare with 0.05. 5) conclude the difference is statistically significant if the p-value is less than 0.05 otherwise no statistical difference"See full answer