30 Latest TensorFlow Interview Questions and Answers

1. TensorFlow Interview Questions

In our last TensorFlow tutorial, we have discussed  Top 30 TensorFlow Interview Questions and Answers. Today, we will be discussing the latest TensorFlow Interview Questions and Answers for TensorFlow upcoming interview. This TensorFlow interview tutorial will help you to crack your TensorFlow Interview the first attempt.

So, let’s explore the latest TensorFlow Interview Questions and Answers.

TensorFlow Interview Questions and Answers

30 Logical TensorFlow Interview Questions and Answers

2. Mostly Asked TensorFlow Interview Questions and Answers

After discussing top 30 TensorFlow Interview Questions and Answers, we are ready to face this TensorFlow Logical Interview Questions and Answers.

Q1. How you place operations on a particular device?

You should create the operations within a with tf.device(name): context to place them on a particular device.

Q2. Which client languages are supported in TensorFlow?

TensorFlow supports multiple client languages, the best language being Python. There are experimental interfaces that are available for C++ Java and Go. Bindings for various other languages (such as C#, Julia, Ruby and Scala) are created and supported by the opensource community.

Q3. Do Sessions have a lifetime? What about intermediate tensors?

Resources like as tf.Variable, tf.QueueBase, and tf.ReaderBase; own by a session and may use a significant amount of memory which are released when the session is terminated with tf.Session.close.

Q4. What’s the deal with feeding and placeholders?

Feeding is a phenomenon that allows you to substitute different values for one or more Tensors at the runtime. The feed_dict argument is used to map tf.Tensorobjects to numpy arrays for further executions.

Q5. Why does Session.run() hang when using a reader or a queue?

The tf.ReaderBase and tf.QueueBase classes provide special operations that become blocked since the input isn’t available. They allow building clear input pipelines, by making the computation a little more complicated.

Q6. What is the lifetime of a variable?

A variable is created when you first run the tf.Variable.initializer operation for that variable in a session. It gets destroyed when that tf.Session.close.

Q7. How do variables behave when they are concurrently accessed?

Variables allow concurrency in read/write ops. The variable value may change when the concurrently updates. By default, there is no mutex (mutual exclusion).

Q8. What is the simplest way to send data to TensorBoard?

First of all, you should add summary operations to your graph, and then log them in a log directory. Then, TensorBoard  should be started using:

python tensorflow/tensorboard/tensorboard.py –logdir=path/to/log-directory

Follow this link to learn more about TensorBoard

Q9. What exactly do you know about Bias-Variance decomposition?

It generally uses to decompose problems such as errors that occur during learning in different algorithms. Bias keeps reducing if the data is to be made complex. Trading off the Variance and Bias are very essential to get results that are totally free from errors. 

Q10.  How is k-means clustering different from KNN?

It is an unsupervised learning algorithm used for clustering. On the other hand, the KNN is a structured clustering algorithm. They both share some similarities but users need to label the data in the KNN which is not required in k-means clustering. 

Read Distributed TensorFlow | TensorFlow Clustering 

TensorFlow Interview Questions and Answers for Freshers. Q- 1,2,4,6,8,9

TensorFlow Interview Questions and Answers for Experience. Q- 3,5,7,10

Q11. What exactly Neural Networks are? What are the types of same you are familiar with?

Basically a connection of processing elements which can very large or very small depending on the application, it deployed for. These elements called neurons and generally, two types of networks can be seen in this category. They are Artificial Neural Networks and Biological Neural Networks. The use of artificial neural networks is more common and generally, they are considered for creating machines which are equally powerful to human brains.

Let’s revise Recurrent Neural Network TensorFlow | LSTM Neural Network

Q12. How do you import Tensorflow?

import TensorFlow as tf

Learn how to install TensorFlow.

Q13. What are word embeddings used for and can they be used in TensorFlow?

Word embeddings usually use in Natural Language Processing as a representation of words and they can use in TensorFlow where it also call as word2vec.

Follow this link to learn more about word embedding.

Q14. Name the two models used in word embeddings?

The Continuous Bag of Words (CBOW) model and the skip-gram model

Q15. Write a code to start a simple session for the training?

with tf.Session() as sess:

Q16. Explain the following example.

for the epoch in range(training_epochs):
 for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})

Here, the initializer is run and all the training data fit by running a loop for all the epochs

Q17. How do you see the charts and graphs for your model and what is the URL?

You can view the charts and graphs using TensorBoard by browsing to https://localhost:6006 in your browser.

Q18. What is the confusion matrix?

A confusing matrix comprising of discrete values where each column contains a set of samples that estimated to be a keyword in your training model.

To learn more about the confusion matrix look at audio recognition using TensorFlow.

Q19. Describe the steps to configure a wide and deep model in TensorFlow?

  • Wide model features: Choosing the base columns and crossed columns.
  • Deep model features: Choosing the continuous columns, the dimension for each categorical column, and hidden layer sizes.

Combining these into a single model with DNNLinearCombinedClassifier

Q20. Write a code to display the evaluated values while training your model in TensorFlow. 

print('Results at epoch', (n + 1) * FLAGS.epochs_per_eval)
 print('-' * 30)
 for key in sorted(results):
   print('%s: %s' % (key, results[key]))

TensorFlow Interview Questions and Answers for Freshers. Q- 11,13,14,17,18,19

TensorFlow Interview Questions and Answers for Experience. Q- 12,15,16,20

Q21. What are the imports needed for visualizing the Mandelbrot set in TensorFlow?

import PIL.Image
from io import BytesIO
from IPython.display import Image, display

Follow this link to learn more about Mandelbrot Set

Q22. How do you report a vulnerability in TensorFlow?

The reports about any security issues can send directly to security@tensorflow.org. The report to this email delivered to the security team at TensorFlow. The emails then acknowledged within 24 hours and detailed response is provided within a week along with the next steps.

Q23. What do you use for deploying a lite model file in TensorFlow?

  • Java API: A wrapper around C++ API on Android.
  • C++ API: It loads the TensorFlow Lite model and calls the interpreter.
  • Interpreter: It can use to execute the model. It uses selective kernel loading which is a unique feature of TensorFlow Lite.

You can also implement custom kernels using the C++ API.

Let’s discuss TensorFlow Mobile | TensorFlow Lite: A Learning Solution

Q24. What are placeholders in TensorFlow?

It is an assurity to the TensorFlow that an external value will be provided later.

Q25. What is tf.contrib.learn?
tf.contrib.learn is a TensorFlow library for simplifying the working of machine learning, and it includes:

  • managing data sets
  • managing feeding

Q26. What is input pipeline optimization?

The process flow of your model includes the loading of the image from the disk, converting it to a tensor followed by manipulating the tensor by cropping, padding and then making a batch. The process flow described above is called input pipeline.

Q27. List the two configurations needed to optimize CPU performance?

Intra_op_parallelism and iner_op_parallelism
Read TensorFlow Performance Optimization | Optimize GPU & CPU

Q28. How do you define a cluster in TensorFlow?

cluster = tf.train.ClusterSpec({“local”: [“localhost:2222”, “localhost:2223”]})

Q29. What is the MNIST dataset?

It is a dataset containing information of handwritten digits.
Follow this link for a deep understanding of MNIST Dataset.

Q30. What are the different dashboards in TensorFlow?

Below mentioned are different types of dashboards in TensorFlow:

TensorFlow Interview Questions and Answers

TensorFlow Interview Questions and Answers- Different Dashboards in TensorFlow

a. Histogram
b. Text
c. Distribution
d. Image
e. Audio
f. Graph
g. Embedding

TensorFlow Interview Questions and Answers for Freshers. Q- 21,23,24,26,29,30

TensorFlow Interview Questions and Answers for Experience. Q- 22,25,27,28

So, this was all about the latest TensorFlow interview questions and answers. Hope you like our explanation.

3. Conclusion

Hence, we have completed the frequently asked TensorFlow Interview questions and answers. Thus, this tricky TensorFlow interview questions and answers are helpful for both freshers as well as professionals. We will come back with more interesting topics. Till then stay tuned and post your doubts in the comments section below.

See also – 

TensorFlow Quiz

Leave a Reply

Your email address will not be published. Required fields are marked *

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.