Being one of the world's largest tech and e-commerce companies, it’s no wonder that Amazon has done a lot to integrate machine learning and artificial intelligence applications to optimize its operations and services. Machine learning engineers play a crucial role in Amazon’s data science team. They research, build, and design the AI responsible for machine learning. They also work to maintain and improve existing AI systems.
A typical salary of an Amazon machine learning engineer is $153,661 per year. Understandably, Amazon interviews are as competitive as the salary they offer. Amazon's robust machine learning interview process includes an initial screen, a technical interview, and an on-site interview.
We’ve curated some Amazon machine learning interview questions to help you gauge your preparation level for your Amazon ML interview. Read ahead to learn more!
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This article focuses on Amazon machine learning interview questions to help you prepare for your next Amazon ML interview.
In this article, we’ll cover:
- Sample Amazon Machine Learning Interview Questions and Answers
- Top Amazon Machine Learning Interview Questions for Practice
- Amazon Machine Learning Interview Questions for Experienced Professionals
- FAQs on Amazon Machine Learning Interview Questions
Sample Amazon Machine Learning Interview Questions and Answers
We’ll begin with some sample Amazon machine learning interview questions and answers to get a basic idea of what to expect.
Q1. How would you handle missing or corrupted data in a dataset?
Dropping the rows or columns with the missing or corrupted dataset or replacing them entirely with a different value are two easy ways to handle such a situation. Methods like IsNull(), dropna(), and Fillna() help in accomplishing this task.
Q2. State the applications of supervised machine learning in modern businesses.
Sentiment Analysis, Email Spam Detection, Fraud Detection, and Healthcare Diagnosis are some applications of Supervised Machine Learning.
Q3. Explain the ensemble learning technique in machine learning.
Ensemble learning involves meta-algorithms combining various ML techniques into a single predictive model. The aim of doing that is stacking, bagging, or boosting. That is, to improve predictions, decrease variance, or decrease bias.
Q4. Differentiate between bagging and boosting.
Bagging is a way to merge the same type of predictions, whereas boosting refers to a method of merging different types of predictions. Bagging decreases variance, and boosting decreases bias, not vice versa.
Q5. How is KNN different from K-means clustering?
K-means is an unsupervised learning algorithm, whereas KNN is a supervised learning algorithm. K-means is mainly used for clustering problems, and the KNN algorithm is primarily used for classification and regression problems.
Want to be an ML engineer at Amazon? Check out our post on Amazon Machine Learning Engineer Interview Prep to kickstart your journey.
Top Amazon Machine Learning Interview Questions for Practice
Here are some Amazon machine learning interview questions. Ensure you can solve them before your interview:
- How will you determine which machine learning algorithm to use for a classification problem?
- How does the Amazon recommendation engine work when recommending other things to buy?
- How would you find thresholds for a classifier?
- Differentiate between logistic regression and support vector machines.
- Give an example of using logistic regression over SVM and vice versa.
- What does the F1 score represent?
- Differentiate between correlation and covariance.
- What are some ways to split a tree in a decision tree algorithm?
- State the assumptions needed to use linear regression.
- How do the results change if we use logistic regression over the decision tree in a random forest?
- What does a ROC area under the curve as an integral represent?
- Describe linear regression vs. logistic regression.
- What are the advantages and disadvantages of SVM?
It helps ease interview anxiety to know the Amazon Machine Learning Engineer Interview Process before the interview. Follow the link to learn more.
Amazon Machine Learning Interview Questions for Experienced Professionals
Lastly, let’s take a look at some advanced machine learning interview questions for experienced professionals asked at Amazon:
- Explain the K-means and K Nearest Neighbor algorithms and differentiate between them.
- How are PCA with a polynomial kernel and a single layer autoencoder related?
- Differentiate between Lasso and Ridge regression.
- Explain ICA, CCA, and PCA.
- Given an array of numbers A and a target value T, return indexes of two numbers such that their absolute difference is equal to T.
- Given two dates, count the number of months and days between them.
- When is it better to use classification over regression?
- Design an Email Spam Filter.
- How does pruning work in Decision Trees?
- State some ways of reducing dimensionality.
- How would you get a CCA objective function from PCA?
We hope that this list of Amazon ML interview questions will help you crack your tech interview. Don’t forget to take up some mock interviews and check our list of 50+ Machine Learning Interview Questions for more practice.
FAQs on Amazon Machine Learning Interview Questions
Q1. Is machine learning used in Amazon?
Yes, widely. The aggregation and analysis of purchase data using ML lead Amazon to better forecast the product demand. The ML analysis of the consumer purchase patterns also helps Amazon spot fraudulent purchases.
Q2. What is the type of machine learning that Amazon uses for business?
Amazon ML uses logistic regression: logistic loss function + SGD for binary classification and multinomial logistic regression: multinomial logistic loss + SGD for multiclass classification.
Q3. How much does an Amazon machine learning engineer earn on average?
An Amazon Machine Learning Engineer in the United States typically earns about $153,661 per annum.
Q4. What are the three types of machine learning?
The three types of machine learning are unsupervised, supervised, and reinforcement learning.
Q5. Which algorithms are most widely used in machine learning?
Linear regression, Decision tree, Logistic regression, KNN algorithm, K-means, SVM algorithm, Naive Bayes algorithm, and Random forest algorithm are some of the most widely used algorithms in Machine Learning.
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