Prepare for NVIDIA interview with top data science questions to crack it easily

NVIDIA is one of the top tech companies in the world with its smart innovations as well as technological advancements. It leverages cutting-edge technologies such as artificial intelligence, computer vision, and many others. Meanwhile, NVIDIA does not have any particular data science department. But, data scientists of this tech company leverage these advanced technologies to launch promising products for data centres, self-driving cars, virtual reality, professional visualization, GPUs, and many more. Thus, many professional and aspiring data scientists want to get recruited by this reputed company. Though NVIDIA interview is tough to crack, candidates can get into it successfully. The questions on data science will be at different levels— beginner, intermediate, and expert. Let's explore some of the top data science questions that are asked in the NVIDIA interview.

An interview in NVIDIA takes place in three steps— primary screening, technical screening, and an onsite interview.

Top ten data science questions in NVIDIA interview

1. What are the differences between a true positive and a false positive?

2. Can you implement gradient descent in Tensorflow?

3. You must design a recommendation engine from end-to-end for a data set to production deployment. How would you do it?

4. Explain a decision tree process under the hood.

5. Explain clearly the number of data science projects you have earlier worked on.

6. If there are missing random values in a data set, how will you solve it?

7. Briefly describe the different techniques used for data sampling and examples.

8. How will you explain linear regression with examples to a non-tech layman?

9. How does traditional application programming different from data science?

10. What is the CART algorithm for a decision tree with ANOVA testing?

The above-mentioned data science questions are just a few out of all the topics covered in an NVIDIA interview. To crack this interview, one needs to have a strong understanding of data science, its components, GPUs, and other technical knowledge. Aspiring data scientists in NVIDIA must have prior work experience in chipset technologies, GPUs, smart devices, and many more.

The global data science market is expected to hit US$25.94 billion in 2027 with a CAGR of 26.9% and the market capitalization of NVIDIA is US$586.92 billion. NVIDIA is highly popular for hardware and software products with a focus on data science including data analytics, deep learning training, conversational AI, data centre, edge computing, graphics virtualization, and many more.