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Machine Learning Engineer Interview Questions

Review this list of 292 Machine Learning Engineer interview questions and answers verified by hiring managers and candidates.
  • Apple logoAsked at Apple 
    9 answers
    +5

    "Make current as root. 2 while current is not null, if p and q are less than current, go left. If p and q are greater than current, go right. else return current. return null"

    Vaibhav D. - "Make current as root. 2 while current is not null, if p and q are less than current, go left. If p and q are greater than current, go right. else return current. return null"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +4 more
  • Pinterest logoAsked at Pinterest 
    1 answer

    "The difference between convex and nonconvex functions lies in their mathematical properties and the implications for optimization problems. Convex Functions:A convex function has a shape where any line segment connecting two points on its graph lies entirely above or on the graph. This property ensures that any local minimum is also a global minimum, making optimization straightforward and reliable. Convex functions are critical in machine learning and optimization tasks because of th"

    Alan T. - "The difference between convex and nonconvex functions lies in their mathematical properties and the implications for optimization problems. Convex Functions:A convex function has a shape where any line segment connecting two points on its graph lies entirely above or on the graph. This property ensures that any local minimum is also a global minimum, making optimization straightforward and reliable. Convex functions are critical in machine learning and optimization tasks because of th"See full answer

    Machine Learning Engineer
    Concept
  • Nvidia logoAsked at Nvidia 
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    Machine Learning Engineer
    Coding
    +1 more
  • Google logoAsked at Google 
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    Machine Learning Engineer
    Behavioral
  • Google logoAsked at Google 
    2 answers

    "untuk mengurutkan daftar angka secara efisien saya akan menggunakan aplikasi pengolah angka yaitu excel dengan rumus rumus untuk mempermudah dan mempercepat pengurutan daftar angka"

    Isnadea soraya R. - "untuk mengurutkan daftar angka secara efisien saya akan menggunakan aplikasi pengolah angka yaitu excel dengan rumus rumus untuk mempermudah dan mempercepat pengurutan daftar angka"See full answer

    Machine Learning Engineer
    Analytical
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  • Meta logoAsked at Meta 
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    Machine Learning Engineer
    Data Structures & Algorithms
    +1 more
  • HP logoAsked at HP 
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    Machine Learning Engineer
    Behavioral
    +1 more
  • Apple logoAsked at Apple 

    Coin Change

    IDE
    Medium
    12 answers
    +9

    "The example given is wrong. The 2nd test case should have answer 3, as we can get to 6 by using 3 coins of denomination 2."

    Anmol R. - "The example given is wrong. The 2nd test case should have answer 3, as we can get to 6 by using 3 coins of denomination 2."See full answer

    Machine Learning Engineer
    Coding
    +4 more
  • Harvey AI logoAsked at Harvey AI 
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    Machine Learning Engineer
    Artificial Intelligence
  • Hubspot logoAsked at Hubspot 
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    Machine Learning Engineer
    Concept
    +1 more
  • Adobe logoAsked at Adobe 
    2 answers

    "The rule doesn't work the other way around. If the array is smaller than n, it can still have duplicates. Eg: n=10 , arr = [3,3]"

    Murali M. - "The rule doesn't work the other way around. If the array is smaller than n, it can still have duplicates. Eg: n=10 , arr = [3,3]"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +2 more
  • Pinterest logoAsked at Pinterest 
    1 answer

    "For a dataset with one million data points, a Deep Neural Network (DNN) is almost always the superior choice over a K-Nearest Neighbors (KNN) algorithm. The primary reasons for this preference involve computational efficiency, scalability, and the ability to handle high-dimensional data. 1. Computational Complexity (Inference Time) KNN: It is a "lazy learner." It doesn't actually "learn" a model; instead, it stores the entire dataset. To make a single prediction, it must calcul"

    Woosung J. - "For a dataset with one million data points, a Deep Neural Network (DNN) is almost always the superior choice over a K-Nearest Neighbors (KNN) algorithm. The primary reasons for this preference involve computational efficiency, scalability, and the ability to handle high-dimensional data. 1. Computational Complexity (Inference Time) KNN: It is a "lazy learner." It doesn't actually "learn" a model; instead, it stores the entire dataset. To make a single prediction, it must calcul"See full answer

    Machine Learning Engineer
    Concept
  • Pinterest logoAsked at Pinterest 
    Add answer
    Machine Learning Engineer
    Behavioral
  • Meta logoAsked at Meta 
    1 answer

    "HashMap supports insert, search, delete and retrieve in O(1). It stores data as key value pairs."

    Ina K. - "HashMap supports insert, search, delete and retrieve in O(1). It stores data as key value pairs."See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +1 more
  • Salesforce logoAsked at Salesforce 
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    Machine Learning Engineer
    Behavioral
    +4 more
  • Scale AI logoAsked at Scale AI 
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    Machine Learning Engineer
    Behavioral
  • Amazon logoAsked at Amazon 
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    Machine Learning Engineer
    Concept
  • Netflix logoAsked at Netflix 
    1 answer

    "TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING: TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency. The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"

    Satyam C. - "TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING: TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency. The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"See full answer

    Machine Learning Engineer
    Concept
  • Adobe logoAsked at Adobe 
    9 answers
    +6

    "Less efficient version, yet effective for the interview: def is_palindrome(s: str) -> bool: dim = len(s) if dim str: dim = len(s) if dim < 2: return s left = 0 longest = "" while left < dim: righ"

    Gabriele G. - "Less efficient version, yet effective for the interview: def is_palindrome(s: str) -> bool: dim = len(s) if dim str: dim = len(s) if dim < 2: return s left = 0 longest = "" while left < dim: righ"See full answer

    Machine Learning Engineer
    Data Structures & Algorithms
    +3 more
  • Nvidia logoAsked at Nvidia 
    Add answer
    Machine Learning Engineer
    Concept
    +1 more
Showing 221-240 of 292