Nvidia is a prominent American technology company renowned for its graphics processing units, boasting millions of users around the world. They provide an API called CUDA that allows users to run large parallel programs on their GPUs, and develop AI solutions for their hardware and software. Nvidia seeks data-related roles for range of tasks, including business analytics, internal reporting, and AI product development
The selection procedure at Nvidia typically spans one or two weeks, and involves four distinct stages: an initial technical evaluation, a virtual review of behavioral questions with a representative from Human Resources, and a pair of on-site conversations with the team you aspire to join that will cover a range of subjects both technical and non-technical.
This guide gives an overview of common interview questions for Nvidia technical roles. To prepare for a specific role at Nvidia, check the appropriate guide seen above.
Coding questions come up consistently in Nvidia job interviews for data positions. They are most frequent during data analyst, data scientist and machine learning engineer roles. Let’s look at some example coding questions for Nvidia:
1. Design a function
list_fifths that returns the fifth-largest number from each list in
numlists, a list where each element is a list of at least five numbers. Write a function
list_fifths that returns a list of the fifth-largest number from each element in
numlists. The resulting list should be in ascending order.
2. Create a function
shortest_transformation to find the shortest transformation sequence from
You’re given two words,
end_word,which are elements of
word_list. Write a function
shortest_transformation to find the length of the shortest transformation sequence from
end_word through the elements of
word_list. Only one letter can be changed at a time, and each transformed word must exist inside of
3. Write a function to convert a list of integers into their corresponding Roman numeral representations.
Given the integer list
numswith length n, create a function that converts each integer in the list into its corresponding Roman numeral representation. The function must be able to handle integers up to 1000. The conversion should be based on the Roman numeral symbols and corresponding values provided.
Machine learning questions come up in 42% of Nvidia job interviews across the board. They are most frequent during data analyst (97%), machine learning engineer (42%), and data scientist (26%) interviews. Let’s explore some typical machine learning questions asked by Nvidia in their interviews:
4. What features would you include in a model to predict customer no-shows for a pizza franchise? Running a pizza franchise, you encounter a problem with frequent no-shows after customers place their orders. What variables would you incorporate in a predictive model to address this issue?
5. How can you justify the complexity of a neural network model and explain its predictions to non-technical stakeholders? Assume your manager asks you to develop a model using a neural network to solve a business problem. How would you justify the intricacies of such a model, and convey its predictions to individuals without a technical background?
6. Which machine learning methods would you use to build a chatbot system for frequently asked questions? You aim to construct a chatbot system that, after a user posts a query, returns the closest response from a list of frequently asked questions. What are the various machine learning strategies you would consider for creating this system?
Case studies are present in 39% of Nvidia job interviews. They vary between data analytics and product metric questions. They are most frequently asked during product manager (97%), data scientist (44%), and software engineer (3%) interviews for Nvidia. Let’s see some examples:
7. What are type I and type II errors in hypothesis testing, and how do they differ? In the context of hypothesis testing, explain the concepts of type I and type II errors, and the differences between the two. For bonus points, provide the mathematical representation of the probability of making each type of error.
8. What business health metrics would be essential for a D2C sock-selling ecommerce company? You are responsible for a D2C business that sells socks online. Which business health metrics would you prioritize tracking on a company dashboard?
9. How would you analyze transaction data to identify the cause of a revenue decline? An e-commerce company has seen a reduction in revenue for the past 12 months. Given transaction data such as date of sale, total $ amount paid by the customer, profit margin per unit, quantity of item, item category, item subcategory, marketing attribution source, and % discount applied, how would you analyze the data to pinpoint where the revenue loss is happening?
SQL questions come up in 32% of Nvidia job interviews across the board, but appear in almost all data & business analyst interviews. They are also frequent, though not as much, for data engineering interviews at Nvidia. The following questions might come up in an Nvidia job interview:
10. Write a SQL query to calculate the percentage of total revenue made during the first and last recorded years.
You are working on a yearly report for your company. You have access to an
annual_payments table. Your task is to calculate the percentage of the total revenue made during the first and last years recorded in the table, rounding to two decimal places.
11. Write a SQL query to find the percentage of accounts active on December 31st, 2019, and closed on January 1st, 2020.
You have an
account_status table with daily records for each account. Calculate the percentage of accounts that were active on December 31st, 2019, and were closed the next day, over the total number of accounts active on December 31st. Round the result to two decimal places.
12. Write a SQL query to sample every 4th row from the transactions table, ordered by date.
You are given a
transactions table with date timestamps. Your task is to write a query to sample every 4th row when the data is ordered by date.
Probability and statistics questions are most common during data scientist (26%) and machine learning engineer (21%) job interviews at Nvidia.
13. What are the limitations of relying solely on R-Squared value in model fit analysis? When analyzing the fit of a model for a dataset, choosing to focus exclusively on the R-squared value could have drawbacks. What are those potential pitfalls?
14. How to handle right-skewed distribution while predicting real estate home prices? While building a model to predict real estate prices in a city, it was discovered that the home value data is right-skewed. Discuss if there is a need to address this skewness and, if yes, how it should be handled. Additionally, describe how a left-skewed target distribution would affect your approach.
15. Can you explain the concept of an unbiased estimator with an example for a layman? Unbiased estimators play a crucial role in statistics and data analysis. Can you provide a straightforward explanation of what an unbiased estimator is, along with a simple example that could be understood by a layman?
To prepare for Probability and Statistics interview questions, we recommend the statistics and A/B testing learning path and the probability learning path. These resources cover a wide range of topics, from basic probability concepts to advanced statistical analysis techniques.
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