My overall experience of taking the exam, how I prepared for it, and what I would’ve done differently if I had to take the exam again.
To be honest, I had not known that TensorFlow offers a certification exam until I saw someone tweeted about it on Twitter. I did some research to find out more about the exam and I said to myself: This is going to be my next goal in my journey of exploring the world of machine learning.
In this article, I want to talk about my personal experience of taking the TensorFlow Developer Certification exam and how I prepared for it. I hope that this article will be helpful to anyone of you who are interested in taking the exam in the near future.
Now before I talk about my experience, it makes sense to talk first about what the TensorFlow Developer Certification exam actually is.
What is the TensorFlow Developer Certification?
As you might already know, Google released an open-source software library for machine learning applications called TensorFlow back in 2015. TensorFlow is one of the most popular deep learning library out there right now that enables you to easily build and deploy different kinds of deep learning models at scale.
The TensorFlow Developer Certification exam allows you to showcase your practical skill to build various models to tackle different kinds of machine learning problems using TensorFlow. If you look at the Candidate Handbook of this certification exam, you’ll soon know that you’d be expected to solve problems related to structured data or unstructured data like images and texts.
As this is a practical exam using TensorFlow, then it assumes that you already know the general concepts behind Shallow Neural Networks, Deep Neural Networks, Convolutional Neural Networks, and Sequence Models. During the exam, you’d be expected to implement these machine learning concepts with TensorFlow.
The exam itself cost $100 per trial meaning that if you fail the exam, you need to pay the exam fee again for every trial you make. If you failed at the first attempt, you can retake the exam 14 days after your first one. If you failed at the second attempt, you need to wait for two months before you’re allowed to do your third attempt.
You’ll be given 5 hours to solve different problems within PyCharm environment and soon after you end the exam, you will get a direct notification via E-Mail whether you’ve passed the exam or not. If you’ve passed the exam, your certificate will be sent to you a couple of days later and it will expire after 3 years.
Why Did I Take the Certification
As a person who wants to continuously develop the skill to solve different kinds of machine learning problems, no doubt that TensorFlow is one of the fundamental tools that I often use.
The idea of having a major goal to take the TensorFlow Developer exam really motivates me to continuously develop my skill on how to utilize TensorFlow to solve machine learning problems. Plus, knowing that there is going to be a time constraint while taking the exam really makes it more challenging. The more I know how challenging my goal is going to be, the harder I will learn to prepare for it.
Another advantage is that if you’re trying to break into an AI industry, having a TensorFlow certification certainly will give you an additional credential, although I can’t guarantee that with a certificate alone is enough for you to break into AI. You can share your certificate in your resume, LinkedIn, or GitHub.
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