Interview Questions

Review this list of 4,348 interview questions and answers verified by hiring managers and candidates.
  • +7

    "Agile methodologies are chosen by organizations to enhance delivery speed, integrate shorter feedback loops, and provide incremental value to customers. However, the suitability of Agile depends on several factors, including the nature of the work, team structure, and organizational objectives. From my experience, for teams focused on new feature development, Scrum tends to be more effective. Scrum’s structured cadence—emphasizing backlog grooming, sprint planning, and regular retrospective"

    Manik K. - "Agile methodologies are chosen by organizations to enhance delivery speed, integrate shorter feedback loops, and provide incremental value to customers. However, the suitability of Agile depends on several factors, including the nature of the work, team structure, and organizational objectives. From my experience, for teams focused on new feature development, Scrum tends to be more effective. Scrum’s structured cadence—emphasizing backlog grooming, sprint planning, and regular retrospective"See full answer

    Program Sense
    Project Management
    +1 more
  • Data Analyst
    Coding
    +1 more
  • " import pandas as pd def findimprovingstudents(transcript: pd.DataFrame) -> pd.DataFrame: summary = transcript.pivottable(index='studentid', values = 'yearlygpa', aggfunc = 'sum',columns = 'year').resetindex() summary['average_gpa'] = round((summary[2023] + summary[2022] + summary[2021])/3,2) return summary(summary[2023] > summary[2022]) & (summary[2022] > summary[2021])] #yn > yn-1, yn-1 > yn-2, yn-3 debug your co"

    Caleb S. - " import pandas as pd def findimprovingstudents(transcript: pd.DataFrame) -> pd.DataFrame: summary = transcript.pivottable(index='studentid', values = 'yearlygpa', aggfunc = 'sum',columns = 'year').resetindex() summary['average_gpa'] = round((summary[2023] + summary[2022] + summary[2021])/3,2) return summary(summary[2023] > summary[2022]) & (summary[2022] > summary[2021])] #yn > yn-1, yn-1 > yn-2, yn-3 debug your co"See full answer

    Coding
    Data Analysis
  • 🧠 Want an expert answer to a question? Saving questions lets us know what content to make next.

  • " import pandas as pd from datetime import datetime def findfastestlike(log: pd.DataFrame) -> pd.DataFrame: log=log.sortvalues(['userid','timestamp']) #get the prev event, time by user log['prevevent'] = log.groupby('userid')['event'].shift(1) log['prevtimestamp'] = log.groupby('userid')['timestamp'].shift(1) True only on rows where the previous event was a login and the current event is a like log['loginlike'] = (log['prevevent'] == 'log"

    Sean L. - " import pandas as pd from datetime import datetime def findfastestlike(log: pd.DataFrame) -> pd.DataFrame: log=log.sortvalues(['userid','timestamp']) #get the prev event, time by user log['prevevent'] = log.groupby('userid')['event'].shift(1) log['prevtimestamp'] = log.groupby('userid')['timestamp'].shift(1) True only on rows where the previous event was a login and the current event is a like log['loginlike'] = (log['prevevent'] == 'log"See full answer

    Coding
    Data Analysis
  • " debug your code below departments = pd.DataFrame({ 'id': [1, 2, 3, 4, 5], 'name': ['Reporting', 'Engineering', 'Marketing', 'Biz Dev', 'Silly Walks'] }) employees = pd.DataFrame({ 'id': [1, 2, 3, 4, 5, 6], 'first_name': ['John', 'Ava', 'Cailin', 'Mike', 'Ian', 'John'], 'last_name': ['Smith', 'Muffinson', 'Ninson', 'Peterson', 'Peterson', 'Mills'], 'salary': [20000, 10000, 30000, 20000, 80000, 50000], 'department_id': [1, 5, 2, 2, 2, 3] }) projects = p"

    Sean L. - " debug your code below departments = pd.DataFrame({ 'id': [1, 2, 3, 4, 5], 'name': ['Reporting', 'Engineering', 'Marketing', 'Biz Dev', 'Silly Walks'] }) employees = pd.DataFrame({ 'id': [1, 2, 3, 4, 5, 6], 'first_name': ['John', 'Ava', 'Cailin', 'Mike', 'Ian', 'John'], 'last_name': ['Smith', 'Muffinson', 'Ninson', 'Peterson', 'Peterson', 'Mills'], 'salary': [20000, 10000, 30000, 20000, 80000, 50000], 'department_id': [1, 5, 2, 2, 2, 3] }) projects = p"See full answer

    Data Analyst
    Coding
    +1 more
  • "The question is incomplete --- the code only passes if you return the data frame sorted by BOTH department name AND rank. While in the problem description, it mentions to only rank by department name: "The results should be ordered by department name." Not a big difference I know, but students shouldn't need to look into the solution to get the necessary knowledge to answer the question."

    Chao peter Y. - "The question is incomplete --- the code only passes if you return the data frame sorted by BOTH department name AND rank. While in the problem description, it mentions to only rank by department name: "The results should be ordered by department name." Not a big difference I know, but students shouldn't need to look into the solution to get the necessary knowledge to answer the question."See full answer

    Coding
    Data Analysis
  • +6

    "Hi, my solution gives the exact numerical values as the proposed solution, but it doesn't pass the tests. Am I missing something, or is this a bug? def findrevenueby_city(transactions: pd.DataFrame, users: pd.DataFrame, exchange_rate: pd.DataFrame) -> pd.DataFrame: gets user city for each user id userids = users[['id', 'usercity']] and merge on transactions transactions = transactions.merge(user_ids, how='left"

    Gabriel P. - "Hi, my solution gives the exact numerical values as the proposed solution, but it doesn't pass the tests. Am I missing something, or is this a bug? def findrevenueby_city(transactions: pd.DataFrame, users: pd.DataFrame, exchange_rate: pd.DataFrame) -> pd.DataFrame: gets user city for each user id userids = users[['id', 'usercity']] and merge on transactions transactions = transactions.merge(user_ids, how='left"See full answer

    Data Analyst
    Coding
    +1 more
  • +3

    "Schema is wrong - id from product is mapped to id from transactions, id from product should point to product_id in transcations table"

    Arshad P. - "Schema is wrong - id from product is mapped to id from transactions, id from product should point to product_id in transcations table"See full answer

    Data Analyst
    Coding
    +1 more
  • +7

    "df.loc[ isin()] is the crucial part of the solution."

    Sean L. - "df.loc[ isin()] is the crucial part of the solution."See full answer

    Coding
    Data Analysis
  • +11

    "def lowestearningemployees(employees: pd.DataFrame, n=3) -> pd.DataFrame: your code goes here sortedempl = employees.sortvalues('salary') lee = sorted_empl.iloc[:n,:] return lee `"

    Anonymous Toad - "def lowestearningemployees(employees: pd.DataFrame, n=3) -> pd.DataFrame: your code goes here sortedempl = employees.sortvalues('salary') lee = sorted_empl.iloc[:n,:] return lee `"See full answer

    Coding
    Data Analysis
  • Statistics & Experimentation
  • "Each flip is independent. P(10 heads)=(1/2​)^10"

    Vineet M. - "Each flip is independent. P(10 heads)=(1/2​)^10"See full answer

    Statistics & Experimentation
  • Product Manager
    Product Design
  • "To define the success metrics for Facebook Ads, I’ll outline: Who the users are What their goals and needs are How success is measured across their journey Who are the users: Advertisers: Individuals or businesses running ad campaigns to achieve business objectives. Potential Buyers: Users exposed to ads who may engage, click, or make purchases. User journey and Success metrics: Advertisers: Acquisition:Onboarding Completion Rate: % of advertisers completing"

    Idit G. - "To define the success metrics for Facebook Ads, I’ll outline: Who the users are What their goals and needs are How success is measured across their journey Who are the users: Advertisers: Individuals or businesses running ad campaigns to achieve business objectives. Potential Buyers: Users exposed to ads who may engage, click, or make purchases. User journey and Success metrics: Advertisers: Acquisition:Onboarding Completion Rate: % of advertisers completing"See full answer

    Product Manager
    Analytical
    +1 more
  • +3

    "Clarify Questions: Is this app in Facebook app? or we build a different app for this? - Yes, this is in FB app now. Will we have monetization in the first launch or launch it for free first? - Free first, but there is a potential monetization in the future. Context: we will launch Facebook Jobs in two weeks, indicating that this is a new product. I want to step back a bit, why this is important for Meta. Meta mission is to make it easier to build community and bring the world closer"

    Nayla D. - "Clarify Questions: Is this app in Facebook app? or we build a different app for this? - Yes, this is in FB app now. Will we have monetization in the first launch or launch it for free first? - Free first, but there is a potential monetization in the future. Context: we will launch Facebook Jobs in two weeks, indicating that this is a new product. I want to step back a bit, why this is important for Meta. Meta mission is to make it easier to build community and bring the world closer"See full answer

    Product Manager
    Analytical
  • +42

    "Context / Goal: Have we seen this decrease in response time over a while (week/month) or short time (day/hours) - Over the last few months How is Employer Response time calculated? - Average time from an employee applying for a job to an employer responding - This isn’t accounting for the increase in listings / number of applicants but does it need to? Was there a change to how response time is calculated? - No Can job listings be duplicates? - Yes, but not likely i"

    Nikitha G. - "Context / Goal: Have we seen this decrease in response time over a while (week/month) or short time (day/hours) - Over the last few months How is Employer Response time calculated? - Average time from an employee applying for a job to an employer responding - This isn’t accounting for the increase in listings / number of applicants but does it need to? Was there a change to how response time is calculated? - No Can job listings be duplicates? - Yes, but not likely i"See full answer

    Product Manager
    Analytical
    +1 more
  • +11

    "Top three most important variables Uber could use to estimate the ETA for passenger pickup, beyond the ETA on Google Maps can be: Driver related - sometimes driver cancel rides after they are assigned or agree to pickup, especially at odd hours. Sometimes, they can also cancel midway through their journeys. Other factor can be that they drive slower or faster than google maps ETA. Car related - The car may be old or not maintained well than other cars. It can be CNG, Diesel, petrol or elec"

    Malvika S. - "Top three most important variables Uber could use to estimate the ETA for passenger pickup, beyond the ETA on Google Maps can be: Driver related - sometimes driver cancel rides after they are assigned or agree to pickup, especially at odd hours. Sometimes, they can also cancel midway through their journeys. Other factor can be that they drive slower or faster than google maps ETA. Car related - The car may be old or not maintained well than other cars. It can be CNG, Diesel, petrol or elec"See full answer

    Analytical
Showing 1061-1080 of 4348