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65+ Machine Learning Engineer Interview Questions And Answers

Engineering ➔ Machine Learning Engineer Job Interview Questions and Answers

Machine Learning Engineer related interview test questions and answers guide. The one who provides the best answers with a perfect presentation is the one who wins the job hunting race. Learn more about Machine Learning Engineer and get preparation for the new job interview

❅ Do you know what is kernel SVM?
❅ Tell me how would you implement a recommendation system for our company’s users?
❅ Tell me how do you choose an algorithm for a classification problem?
❅ Tell us what’s the F1 score? How would you use it?
❅ Explain what are some methods of reducing dimensionality?
❅ Tell me what is a recommendation system?
❅ Tell us where do you usually source datasets?
❅ What is the difference between L1 and L2 regularization?
❅ Can you pick an algorithm. Write the psuedo-code for a parallel implementation?
❅ Tell me what is the difference between bias and variance?
❅ Tell us how do you handle missing or corrupted data in a dataset?
❅ Tell us do you have research experience in machine learning?
❅ Tell us which do you think is more important: model accuracy or model performance?
❅ Tell us how would you approach the “Netflix Prize” competition?
❅ Tell us what’s a Fourier transform?
❅ Tell us what are some differences between a linked list and an array?
❅ Explain how do you think Google is training data for self-driving cars?
❅ Tell us what’s the difference between Type I and Type II error?
❅ Tell us when should you use classification over regression?
❅ Tell us how do deductive and inductive machine learning differ?
❅ Tell us what kind of problems does regularization solve?
❅ Tell us what is decision tree classification?
❅ Tell me do you have experience with Spark or big data tools for machine learning?
❅ Tell us why is “Naive” Bayes naive?
❅ Tell me how much data will you allocate for your training, validation and test sets?
❅ Tell me what is supervised versus unsupervised learning?
❅ Tell us how can we use your machine learning skills to generate revenue?
❅ Tell us an example where ensemble techniques might be useful?
❅ Tell me what is precision and recall?
❅ Can you list some use cases where classification machine learning algorithms can be used?
❅ Explain me what’s your favorite algorithm, and can you explain it to me in less than a minute?
❅ Tell me how is KNN different from k-means clustering?
❅ Tell us why is Naïve Bayes machine learning algorithm naïve?
❅ Tell us what is your training in machine learning and what types of hands-on experience do you have?
❅ Tell us what do you think of our current data process?
❅ Tell us how do you ensure you’re not overfitting with a model?
❅ Do you know which is more important to you– model accuracy, or model performance?
❅ Explain me what is Bayes’ Theorem? How is it useful in a machine learning context?
❅ Please explain what is deep learning, and how does it contrast with other machine learning algorithms?
❅ Do you know what’s the “kernel trick” and how is it useful?
❅ Please explain what is deep learning?
❅ Tell us how will you know which machine learning algorithm to choose for your classification problem?
❅ Tell us which one would you prefer to choose – model accuracy or model performance?
❅ Explain me what cross-validation technique would you use on a time series dataset?
❅ Explain me what’s the trade-off between bias and variance?
❅ Tell us how is a decision tree pruned?
❅ Explain me how would you handle an imbalanced dataset?
❅ Tell us what evaluation approaches would you work to gauge the effectiveness of a machine learning model?
❅ Explain me a hash table?
❅ Tell me what are the last machine learning papers you’ve read?
❅ Explain me what is machine learning?
❅ Tell us how do classification and regression differ?
❅ Can you explain what is the difference between inductive machine learning and deductive machine learning?
❅ Explain me machine learning in to a layperson?
❅ Tell me what is the most frequent metric to assess model accuracy for classification problems?
❅ Tell me how a ROC curve works?
❅ Tell us what’s the difference between a generative and discriminative model?
❅ How would you evaluate a logistic regression model?
❅ Tell us which data visualization libraries do you use? What are your thoughts on the best data visualization tools?
❅ Tell me what are your favorite use cases of machine learning models?
❅ Tell us how do bias and variance play out in machine learning?
❅ Tell me how does deep learning contrast with other machine learning algorithms?
❅ Can you name some feature extraction techniques used for dimensionality reduction?
❅ Tell us when will you use classification over regression?
❅ Tell us what is the difference between supervised and unsupervised machine learning?

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