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Interview Questions

Review this list of 348 interview questions and answers verified by hiring managers and candidates.
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    Video answer for 'Why PMM?'
    Behavioral
  • Add answer
    Video answer for 'Analyze Monthly Customer Transactions'
    Data Engineer
    Coding
    +3 more
  • TikTok logoAsked at TikTok 
    Add answer
    Video answer for 'Split an array into equal sum subarrays'
    Data Engineer
    Data Structures & Algorithms
    +1 more
  • Accenture logoAsked at Accenture 
    4 answers
    Video answer for 'What is a p-value?'
    +1

    "It is the smallest level of significance at which the null hypothesis gets rejected"

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

    Data Scientist
    Statistics & Experimentation
    +2 more
  • Microsoft logoAsked at Microsoft 
    1 answer
    Video answer for 'How would you design and launch a TinyURL-like product?'

    "Missing launch. Only design is covered."

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

    Product Design
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    Video answer for 'Predict Results from a Fair Coin'
    Coding
  • Add answer
    Video answer for 'Design a data warehouse schema for Amazon.'
    Data Engineer
    Data Modeling
  • Amazon logoAsked at Amazon 
    2 answers
    Video answer for 'What are common linear regression problems?'

    "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"

    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

    Data Scientist
    Analytical
    +2 more
  • Meta logoAsked at Meta 
    4 answers
    Video answer for 'Design an API for searching a folder.'
    Software Engineer
    System Design
    +1 more
  • Meta logoAsked at Meta 
    1 answer
    Video answer for 'What is a recently effective campaign you found?'

    "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."

    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

    Behavioral
  • Intuit logoAsked at Intuit 
    2 answers
    Video answer for 'Design Reddit.'

    "Graphic design is the process of visual communication and problem-solving through the use of typography, photography"

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

    Software Engineer
    Product Design
    +1 more
  • AMD logoAsked at AMD 
    15 answers
    Video answer for 'Find the number of rotations in a circularly sorted array.'
    +10

    "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 `"

    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

    Software Engineer
    Data Structures & Algorithms
    +1 more
  • "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"

    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

    Statistics & Experimentation
  • Uber logoAsked at Uber 
    10 answers
    Video answer for 'A knapsack has a maximum capacity C and there are n items each with weight w[i] and value v[i]. Maximize the knapsack value without exceeding capacity.'
    +7

    " 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"

    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

    Software Engineer
    Data Structures & Algorithms
    +2 more
  • Stripe logoAsked at Stripe 
    Add answer
    Video answer for 'How would you communicate layoffs to affected individuals?'
    Engineering Manager
    Behavioral
  • Teoco logoAsked at Teoco 
    1 answer
    Video answer for 'Could you provide an example of a go-to-market campaign?'

    "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?"

    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

    Behavioral
  • "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"

    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

    Statistics & Experimentation
  • Google logoAsked at Google 
    2 answers

    "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"

    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

    Product Strategy
  • 1 answer
    Video answer for 'Have you ever had to work with poor-quality data or suggest new tracking?'

    "“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.”"

    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

    Business Analyst
    Data Analysis
    +3 more
  • Truecaller logoAsked at Truecaller 
    1 answer
    Video answer for 'How would you improve engagement for the Truecaller app?'

    "One of the better videos!"

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

    Analytical
    Execution
    +1 more
Showing 261-280 of 348
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