If you aspire to work as a data scientist at Amazon, preparing for Amazon data scientist interview questions will help you crack the rigorous Amazon interview process.
Unlike many other tech companies, Amazon's interview process is unique. They take a candidate-first approach, asking pertinent questions and assisting with necessary resources. As a result, preparing for this interview can be challenging. We have compiled a list of frequently asked Amazon data scientist interview questions to help you with your prep.
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We’ll cover the following topics in this article:
- Amazon Data Scientist Interview Questions for Beginners
- Amazon Data Scientist Interview Questions on Machine Learning
- Amazon Data Scientist Interview Questions on Deep Learning
- Sample Amazon Data Scientist Interview Questions on Coding
- FAQs on Amazon Data Scientist Interview Questions
Amazon Data Scientist Interview Questions for Beginners
Here are a few basic Amazon interview questions for data scientists that beginners and freshers will find helpful:
Q1. Explain Data Science.
Data Science is a mix of various tools, algorithms, and Machine Learning principles. The main goal is to discover hidden patterns from the raw data.
Q2. Differentiate between supervised and unsupervised learning.
- The input data is labeled.
- It uses a training data set.
- This enables classification and regression.
- It is used for prediction.
- The input data is not labeled.
- It uses an input data set.
- This enables classification, density estimation, and dimension reduction.
- It is used for analysis.
Q3. What are the types of selection bias?
The four types of selection bias are:
- Sampling Bias
- Time interval
Q4. Why do we use a summary function?
This is used to summarize all the numeric data in the data frame. For instance: the describe() function can summarize all data values. columnname.describe() will show the following values of all the numeric data in the column-
- Standard deviation
Q5. Why is R used in data visualization?
R is used in data visualization because it has inbuilt functions and libraries. These libraries have ggplot2, leaflet, and lattice. R also helps in exploratory data analysis along with feature engineering.
Additional Amazon Data Scientist Interview Questions for Freshers
- Tell us something about AWS.
- Was there any time when you had to deal with ambiguity?
- Have you ever worked in a team?
- Which project have you worked on?
- How do you plan your time management strategies?
- What is a confusion matrix?
- Describe Markov Chains.
- What is a true positive rate and a false-positive rate?
- What is dimensionality reduction?
- How do you find the RMSE and MSE linear regression models?
Amazon Data Scientist Interview Questions on Machine Learning
A typical Amazon data scientist interview also contains the following Machine Learning interview questions:
Q1. What is Machine Learning?
Machine Learning is the study and construction of algorithms. It is closely related to computational statistics. It is used for devising complex models and algorithms that lend to a commercial prediction known as predictive analysis.
Q2. Explain “Naïve” in Naive Bayes.
This algorithm is based on the Bayes theorem. It describes the probability of an event based on the prior knowledge of conditions related to that event.
Q3. What is pruning?
Pruning is a technique in Machine Learning and search algorithms that reduce the size of decision trees. It removes sections of the tree that provide little power to classify instances. Thus, the removal of sub-nodes of a decision node is called pruning.
Q4. What is Linear Regression?
This is a statistical technique where the score of Y is predicted from the second variable, X. Here, X is the predictable variable, and Y is the criterion variable.
Q5. Explain the drawbacks of a linear model.
Drawbacks of the linear model are-
- The assumption of linearity of errors.
- It is not useful for binary outcomes or count outcomes.
- It cannot solve the overfitting problem.
Additional Amazon Data Scientist Interview Questions on Machine Learning
- Briefly describe the Decision Tree Algorithm.
- What are Entropy and information gained in the Decision Tree Algorithm?
- Differentiate between Regression and classification ML techniques.
- What are Recommender Systems?
- How can outlier values be treated?
- What are the various steps in an analytics project?
- During analysis, how do you treat missing values?
- How will you define the number of clusters in a clustering algorithm?
- Describe Ensemble learning.
- How do you work towards a random forest?
Recommended Reading: Microsoft Data Science Interview Questions
Amazon Data Scientist Interview Questions on Deep Learning
Scroll down to find Amazon data scientist interview questions related to Deep Learning:
Q1. What is the difference between Machine Learning and Deep Learning?
Machine Learning in computer science enables the computer to learn without explicit programming. It can be categorized into the following categories:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning
Deep Learning employs a complex set of algorithms modeled after the human brain. This allows unstructured data such as documents, images, and text to be processed.
Q2. Why do you think Deep Learning is gaining more popularity?
Deep Learning has existed for a long term; however, the breakthroughs using this technique came recently. This is because:
- It increased the amount of data generated through several sources.
- The growth in hardware resources required to run different models.
Q3. Define reinforcement learning.
This method teaches what to do and how to map situations to actions. Its purpose is to increase the numerical reward signal. Interestingly, it is inspired by the learning process of human beings as it is also based on the reward-penalty model.
Q4. What is the structure of an Artificial Neural Network?
Like biological neural networks, Artificial Neural Networks work on the same principles. It consists of inputs that get processed.
Q5. Explain Cost Function.
The Cost Function is also referred to as "loss" or "error." It is a measure to check how good the model's performance is. It's used to compute the error of the output layer during backpropagation.
Additional Amazon Data Scientist Interview Questions on Deep Learning
- What is Deep Learning?
- Explain Neural Network fundamentals.
- Describe Neural Networks.
- What are Hyperparameters?
- Differentiate between Epoch, Batch, and Iteration in Deep Learning.
- What are the different layers on CNN?
- How does Pooling work on CNN?
- Explain how the LSTM network works.
- Define the Gradient Descent.
- What are exploding gradients?
For more data science interview questions, read this article.
If you are an aspiring software developer or a tech lead, these questions can help you ace your Amazon tech interview. So, don’t delay your tech interview prep and practice these Amazon data scientist interview questions. Check out these sample questions for quick coding interview prep.
Sample Amazon Data Scientist Interview Questions on Coding
Here are a few commonly asked Amazon data scientist interview coding questions:
- Describe JOINs and SQL.
- What is the most advanced query you’ve ever written?
- White a SQL code to explain the month-to-month user retention?
- You are given a list of integers, and you need to find a certain element. Which algorithm will you use?
- Which algorithm would you use to search a long list for the four elements if you have a long and short sorted list?
- Write a Python function that displays first N Fibonacci numbers.
- What are the processes of improving a classification model of low precision?
- You are given time-series data by the month with large data records. How will you find differences between this month and the previous month?
- How do you inspect missing data?
- When is the missing data inspection important?
Also, read Python Data Science Interview Questions to learn about the common Python-based interview questions.
Getting a job at Amazon can be a fantastic move for your career. So, start preparing for the Amazon data scientist interview questions, and you'll be able to land that dream job you've always wanted.
FAQs on Amazon Data Scientist Interview Questions
Q1. How to prepare for Amazon data scientist interview questions?
Some effective tips from the experts on Amazon data scientist interview questions are: be prepared for interpersonal and behavioral questions, use the STAR method while responding, answer in detail, do not hesitate and limit your skills or experiences, it is okay to share failures, have a clear idea of why you want to work at Amazon, ask questions to the interviewer, study Amazon’s leadership principles, and research on Amazon’s work culture.
Q2. What are the different stages of the data scientist interview at Amazon?
The Amazon data scientist interview process generally has three stages- Initial Screening, Technical Screening, and On-site Round.
Q3. How long is the interview process at Amazon for a data scientist?
The initial screening process is for half an hour. And the technical round can take up to an hour to complete. At the same time, the on-site interview takes 6 hours to complete.
Q4. Is the data scientist job at Amazon a good career?
Amazon is a top-tier technology company that many people want to work for. Working as a data scientist at Amazon will expose you to a wide range of perks, experiences, and benefits.
Q5. How much does a data scientist earn in the US?
A data scientist earns an average salary of $109,666 per year in the US.
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