Top 20 TensorFlow Interview Questions

When hiring for a Data Science and AI role, interviewers specifically test candidates for their Python and Artificial Intelligence skills. While AI is a vast topic and can invite questions from varying ends of the spectrum. Interviewers usually ask questions from some of the most-used libraries, concepts, and applications. One such popular topic in AI for Data Science and AI interviews is TensorFlow

In continuation to our series on Data Science interview questions, in this article, we share with you some popular questions on TensorFlow. These top 20 TensorFlow interview questions will give you an idea of the type of common questions asked, help you brush up on your basics, and crack the Data Scientist interviews. Earlier we shared with you popular Machine Learning interview questions for Data Scientists. 

Let’s get started. 

What is TensorFlow?

Tensorflow was first made public in 2015. It is a popular python framework used to create deep learning applications. It uses data and performs programming on tensors and lets the user create a flowchart that works on input data.  

The word TensorFlow consists of two parts – Tensor and Flow. A Tensor is defined as a mathematical concept that helps generalize vectors, scalars, and matrices. A tensor is used for the multidimensional representation of the relationship between functions of the coordinate system. The word flow represents the operations that are performed tensors

TensorFlow Interview Questions

  1. Name the different types of tensors.
  2. What do you understand by Tensorboard?
  3. Name some disadvantages of using TensorFlow. 
  4. How would you deal with overfitting in TensorFlow?
  5. Explain TensorFlow Serving. 
  6. How will you define deep speech?
  7. Explain how TensorFlow uses python API. 
  8. Name some APIs outside the TensorFlow project. 
  9. Define the ROC curve and explain its working.
  10. Define a graph explorer in TensorFlow. 
  11. How is PyTorch different from TensorFlow?
  12. Why are estimators used in TensorFlow?
  13. How can TensorBoard be used without installing TensorFlow?
  14. State 3 differences between CNN and RNN.
  15. Create a tensor using the constant function in TensorFlow. 
  16. What do you understand by TensorFlow JS?
  17. Write the code to check the TensorFlow version using python. 
  18. Why is FeedDictFlow used in TensorFlow?
  19. What factors should you consider while implementing a random forest algorithm in TensorFlow?
  20. Explain the difference between type1 and type2 errors

We hope you found these TensorFlow interview questions useful. Practice these questions and share your answers with us in the comments section. To know more about how to crack a Data Scientist interview, check out Radhika’s success story, where she shares her interview experience with Tiger Analytics. 

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