Interview Questions

Farza S. - "It is the smallest level of significance at which the null hypothesis gets rejected"See full answer

Miguel C. - "Missing launch. Only design is covered."See full answer

Ilnur I. - "I can try to summarize their discussion as I remembered. Linear regression is one of the method to predict target (Y) using features (X). Formula for linear regression is a linear function of features. The aim is to choose coefficients (Teta) of the prediction function in such a way that the difference between target and prediction is least in average. This difference between target and prediction is called loss function. The form of this loss function could be dependent from the particular real"See full answer

U K. - "Additional of COVID disclaimers to COVID related reels in instagram has helped users to navigate the crisis effectively as well as get the facts addressed regarding the vaccine."See full answer

Abdurhman M. - "Graphic design is the process of visual communication and problem-solving through the use of typography, photography"See full answer

Aikya S. - "from typing import List def find_rotations(nums: List[int]) -> int: left = 0 right = len(nums) - 1 if len(nums) < 2 or nums[left] < nums[right]: return 0 while(left + 1 < right): mid = left + (right - left) // 2 if nums[mid] < nums[left]: right = mid else: left = mid return left + 1 `"See full answer

Connor W. - "In the expected value of a coupon problem, you calculated variance of a binomial distribution, and used the satandard deviation, square root of variance, to calculate the confidence interval. Will that approach work the same here? For fair coin: (Heads = 0, tails = 1) Var = 10 * (.5)(1-.5) = 2.5 Stdev = Sqrt(2.5) = 1.581 Mean = 5 Z-score = (Observed Val - Mean) / Stdev = (10 - 5) / 1.581 = 3.164 P val = 0.0008% (Slightly different from the video's solution of 0.00097) Pros of this approach: It"See full answer

Rick E. - " DP Solution Time: O(W * C) Space: O(W * C) from typing import List def knapsack(weight: List[int], values: List[int], cap: int) -> int: dp = [[0] * (cap + 1) for _ in range( len(values) + 1 )] for i in range(1, len(weight)+1): for c in range(1, cap + 1): curr_weight = weight[i - 1] curr_value = values[i - 1] include = 0 exclude = dpi-1 if c - curr_weight >= 0: include = curr_valu"See full answer

Vivek S. - "Hey team, the title says a "GTM campaign" whereas the video is about "favorite marketing campaign" example. Are they supposed to be the same? If not, can you please fix it?"See full answer

Aman M. - "Apoorva Tai is the best. Wash-off effect can be applied on switchback if 1-hour time-block is persisted with, where the first 10-15 minutes of a switchback is ignored"See full answer

Anjan M. - "Product - Google Glass Undefined Core Segment & Jobs-to-Be-Done - Google pitched Glass to “everyone” but never clarified a must-have use case for any one group. Improper Category Context - It was positioned as a lifestyle product, but could not justify a use case that a mobile device could not already do. Premium Pricing of a Prototype- Explorer Edition cost 1500 usd. This gap alienated hobbyists and discouraged volume sales. Privacy Backlash- The Always-on camera r"See full answer

Kusheta K. - "“I once worked with a dataset that had missing and inconsistent tracking. I first evaluated data quality, cleaned what was reliable, and documented assumptions. Then I collaborated with engineers to implement improved event tracking. This ensured more accurate analysis in the future and improved decision-making.”"See full answer

Alex S. - "One of the better videos!"See full answer