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Concept Interview Questions

Review this list of 159 Concept interview questions and answers verified by hiring managers and candidates.
  • Deloitte logoAsked at Deloitte 
    3 answers

    "BETWEEN and HAVING clauses in SQL serve different purposes: 1. BETWEEN Clause Used to filter rows based on a range of values. Works with numeric, date, or text values. Can be used with WHERE or HAVING clauses. The range includes both lower and upper bounds. Example: Filtering employees with salaries between 30,000 and 50,000 `SELECT * FROM Employees WHERE salary BETWEEN 30000 AND 50000;` 2. HAVING Clause Used to filter **groups"

    Meenakshi D. - "BETWEEN and HAVING clauses in SQL serve different purposes: 1. BETWEEN Clause Used to filter rows based on a range of values. Works with numeric, date, or text values. Can be used with WHERE or HAVING clauses. The range includes both lower and upper bounds. Example: Filtering employees with salaries between 30,000 and 50,000 `SELECT * FROM Employees WHERE salary BETWEEN 30000 AND 50000;` 2. HAVING Clause Used to filter **groups"See full answer

    Software Engineer
    Concept
    +4 more
  • 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
    Concept
    +2 more
  • Meta logoAsked at Meta 
    1 answer

    "Merge Sort"

    Ankita G. - "Merge Sort"See full answer

    Data Engineer
    Concept
    +1 more
  • Snap logoAsked at Snap 
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    Machine Learning Engineer
    Concept
    +2 more
  • Microsoft logoAsked at Microsoft 
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    Product Manager
    Concept
    +3 more
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  • OpenAI logoAsked at OpenAI 
    2 answers

    "Reinforcement Learning is a type of machine learning where an agent learns to make decisions by trying out different actions and receiving rewards or penalties in return. The goal is to learn, over time, which actions yield the highest rewards. There are three core components in RL: The agent — the learner or decision-maker (e.g., an algorithm or robot), The environment — everything the agent interacts with, Actions and rewards — the agent takes actions, and the environmen"

    Constantin P. - "Reinforcement Learning is a type of machine learning where an agent learns to make decisions by trying out different actions and receiving rewards or penalties in return. The goal is to learn, over time, which actions yield the highest rewards. There are three core components in RL: The agent — the learner or decision-maker (e.g., an algorithm or robot), The environment — everything the agent interacts with, Actions and rewards — the agent takes actions, and the environmen"See full answer

    Machine Learning Engineer
    Concept
    +1 more
  • +2

    "In details: setting k=1 in KNN makes the model fit very closely to the training data, capturing a lot of the data's noise and leading to a model that may not generalize well to unseen data. This results in a high-variance scenario."

    Taha U. - "In details: setting k=1 in KNN makes the model fit very closely to the training data, capturing a lot of the data's noise and leading to a model that may not generalize well to unseen data. This results in a high-variance scenario."See full answer

    Concept
    Machine Learning
  • Airbnb logoAsked at Airbnb 
    2 answers

    "Clarification questions What is the purpose of connecting the DB? Do we expect high-volumes of traffic to hit the DB Do we have scalability or reliability concerns? Format Code -> DB Code -> Cache -> DB API -> Cache -> DB - APIs are built for a purpose and have a specified protocol (GET, POST, DELETE) to speak to the DB. APIs can also use a contract to retrieve information from a DB much faster than code. Load balanced APIs -> Cache -> DB **Aut"

    Aaron W. - "Clarification questions What is the purpose of connecting the DB? Do we expect high-volumes of traffic to hit the DB Do we have scalability or reliability concerns? Format Code -> DB Code -> Cache -> DB API -> Cache -> DB - APIs are built for a purpose and have a specified protocol (GET, POST, DELETE) to speak to the DB. APIs can also use a contract to retrieve information from a DB much faster than code. Load balanced APIs -> Cache -> DB **Aut"See full answer

    Data Scientist
    Concept
    +6 more
  • Anthropic logoAsked at Anthropic 
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    Machine Learning Engineer
    Concept
    +2 more
  • Apple logoAsked at Apple 
    2 answers

    "Hey Grandma, you've had a lot of experience with infants, haven't you? When they were babies, you taught them how to chew in their first six months. This initial phase is like giving them data. Once they learned how to chew, they could handle any food you gave them. Next, you refined their learning by teaching them that they should only chew on food. This is like refining the data so they understand what is relevant. Then, a few months later, they started crawling and walking, learning by observ"

    Hari priya K. - "Hey Grandma, you've had a lot of experience with infants, haven't you? When they were babies, you taught them how to chew in their first six months. This initial phase is like giving them data. Once they learned how to chew, they could handle any food you gave them. Next, you refined their learning by teaching them that they should only chew on food. This is like refining the data so they understand what is relevant. Then, a few months later, they started crawling and walking, learning by observ"See full answer

    Machine Learning Engineer
    Concept
  • Perplexity AI logoAsked at Perplexity AI 
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    Software Engineer
    Concept
    +1 more
  • Google logoAsked at Google 
    5 answers
    +2

    "https://www.freecodecamp.org/news/what-happens-when-you-hit-url-in-your-browser/"

    Kanika - "https://www.freecodecamp.org/news/what-happens-when-you-hit-url-in-your-browser/"See full answer

    Product Manager
    Concept
    +3 more
  • Nike logoAsked at Nike 
    1 answer

    "I did not give the proper ans so gettting rejected"

    Praveen K. - "I did not give the proper ans so gettting rejected"See full answer

    Software Engineer
    Concept
  • Apple logoAsked at Apple 
    Add answer
    Machine Learning Engineer
    Concept
    +1 more
  • Google logoAsked at Google 
    2 answers

    "Grandma! You know how we can look at a picture and know what's in it—like seeing a cat or a dog? Computers can learn to do that too! It's just they use special tricks and math to see and understand pictures or videos. It helps them figure out what's in the pictures, almost like how we do! Almost like giving it eyes to see the world in its own way!"

    Praveen D. - "Grandma! You know how we can look at a picture and know what's in it—like seeing a cat or a dog? Computers can learn to do that too! It's just they use special tricks and math to see and understand pictures or videos. It helps them figure out what's in the pictures, almost like how we do! Almost like giving it eyes to see the world in its own way!"See full answer

    Machine Learning Engineer
    Concept
  • Machine Learning Engineer
    Concept
    +1 more
  • Lyft logoAsked at Lyft 
    2 answers

    "Potential ad creators: Brands Drivers Travel services. Goal: To create a revenue stream. Acquire new users? To potentially develop new products? For now, lets focus on generating revenue as the goal. Potential ad products: Ads from brands you can watch while you are riding. Most riders dont look at the app while riding but we can provide them incentives like 5% off next ride if they watch ads the whole ride and pay it via ad revenue. Drivers can pay Lyft to be matched more w"

    M N. - "Potential ad creators: Brands Drivers Travel services. Goal: To create a revenue stream. Acquire new users? To potentially develop new products? For now, lets focus on generating revenue as the goal. Potential ad products: Ads from brands you can watch while you are riding. Most riders dont look at the app while riding but we can provide them incentives like 5% off next ride if they watch ads the whole ride and pay it via ad revenue. Drivers can pay Lyft to be matched more w"See full answer

    Concept
    Product Design
  • Snap logoAsked at Snap 
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    Machine Learning Engineer
    Concept
  • Add answer
    Video answer for 'How can we tell when a model needs to be refreshed?'
    Concept
    Machine Learning
  • Apple logoAsked at Apple 
    Add answer
    Machine Learning Engineer
    Concept
Showing 41-60 of 159