How To Ace Tensorflow Developer Exam

Author(s): Ömer Özgür

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I have been working in the field of Machine Learning for more than 4 years. Also, I am a self-thought Machine Learning engineer. The education system responded very late to the new paradigm of Deep Learning, and there is so little place to take good education and certification.

I thought that Tensorflow Developer Exam would be a good certification challenge. When you get the Tensorflow Developer Certificate, you can show the world to most essential skills.

In this article, I will discuss what this exam is, how to prepare, some problems you can encounter, and some motivation. Reading the experiences of as many different people as possible will allow you to see the problem from different angles. Let’s get started.

Table of contents:

• Introduction
• Best Course To Take
• Some Tips and Tricks
• My Problems and Solutions
• Final Words

Introduction

I will directly talk about the exam. It is essential to read the guidance on Tensorflow’s website. After you understand what is going on, register for the exam. The exam has a 100 $ fee. Before entering the exam, you should build your Pycharm env in the correct versions of the required libraries. The TensorFlow official website is the best place to find clear tutorials on model training for each type of neural network application.

The exam includes regression, CV, NLP, and time series questions, similar to courses.

There will be 5 questions in the exam. To be successful, you need to score above a certain score. I got 3*(5/5) and 2*(4/5) = 23/25 scores which were enough to pass. You have 5 hours to answer all questions. If everything goes smoothly, the exam could finish in 2 hours.

After ending the exam, in 2 minutes, you will get an email that says, “Congratulations, you have passed the TensorFlow Developer Certificate exam!”. You have to wait a max of 2 weeks to get a certificate.

Best Course To Take

The best course for preparing for the exam is DeepLearning.AI TensorFlow Developer Professional Certificate. If you have a certain level of experience in machine learning, it will be enough for you to solve the code examples in this course.

There are 4 sub-courses in this program. In turn, regression, classification, computer vision, NLP, and Time-series topics are covered. Also, the exam will cover the same topics.

When you finish the course, go to Kaggle or similar platforms. And try to solve different types of problems such as NLP, CV, and time series.

Some Tips and Tricks

Tensorflow Dev exam is also similar to all exams, so it has some tricks. My first trick is to use multiple Gpu’s. This method does not give a big boost, but it seems useful.

When I was reading blogs, it was written that submitting the same model more than once returned different results. This was most likely due to the constant changing of the test set. If you want to get a higher score, you can test the same model more than once.

Using callbacks can be advantageous. For example, ModelCheckpoint continuously saves your best model and you will not have a problem even if your model in training is overfit or underfit.

At the same time, finding the Appropriate Learning Rate is an experimental task. Here, ReduceLROnPlateau can be used. When the loss does not decrease with a certain patience coefficient, it decreases the LR at the determined rate.

Finally, read the question carefully and review the instructions again if you’re stuck on a question.

My Problems and Solutions

First of all, I got a Tensorflow version problem. Currently, the exam uses Tensorflow 2.7.x. I want to use multiple and powerful GPUs to minimize training to do faster experiments and solve more questions.

You can learn about the training speed by trying Tensorflow’s sample CV and NLP questions on your own computer. Also, I recommend that the compute capability of the GPU you will use be above 6.1.

Our team already has Google Colab Pro to use for the exam. But there was a problem. Colab was using Tensorflow 2.8.0, so it could create some problems. I downgraded Tensorflow to 2.7.0, but Cuda did not work.

My last choice was to use AWS. AWS offers prebuilt DeepLearning environments. I set up a g4dn.xlarge, EC2 instance, and build Jupiter notebook that I can connect from my laptop. And our powerful GPU is ready g4 series has Tesla T4 GPU, which is compute capability 7.5.

The version of Tensorflow and other libraries used by the exam changes over time. Colab or AWS can be used when versions match. Even just 1 GPU will do a lot of work.

I have not got any Pycharm related problems. If you read and apply the instructions, everything will be okay.

Final Words

This certification will not open every door for you, but you can achieve more with the self-confidence you gain. You can then join the TensorFlow Certificate Network and see yourself on the map. Education and learning is a never-ending process, after gaining general skills, you should learn information about the field you want to specialize in and develop projects.

If you did everything, solved all types of questions from courses and Kaggle. Trust yourself. You are ready!

Good Luck!

References

[1] https://www.tensorflow.org/certificate

[2] https://www.coursera.org/

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