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Passing the TensorFlow Developer Exam

Sharing my journey from preparation to certification

After I finished my requirements for university, I reassessed myself and set up on a 4-month journey to study AI, following a curated bottom-up curriculum using the books that I bought and some courses. Looking back, this is one of the best decisions I have ever made.

Shoutout to the slack communities that helped me to be accountable in this study — Udacity folks from AI Network and Filipinos from the former AI Study Group PH, and twitter folks.

One of the checkpoints I have included in my curriculum is to pass the TensorFlow Developer Certification Exam. I decided to take it as it challenges me since it is a hands-on exam and it pushes me to actively study TensorFlow.

In this blog, I will share some information regarding the exam that helped me and how I prepared for it.

You can also now view the video version of this article below:

First, we have to set our expectations right. This is a different exam because it is hands-on type which can be taken online and needed to be finished in a 5-hour time limit. This sentence is too compressed, let’s elaborate.

The major platforms I used were Google Colab and PyCharm 2020.2. The use of Google’s Colab is not mandatory but recommended to take advantage of the GPU. This information is also in the guide. This also became a need for me as my laptop hangs and take some time in training neural networks. On the other hand, the specific version to be used for the PyCharm IDE is very important. Make sure it is 2020.2 as of this writing. By following the instructions closely, I did not experience any problems or errors while taking the exam.

The problems along with the other information, which is enough to let you know what you have to do, will be given in the Exam Environment to be installed in PyCharm. There are two cases for which the exam can end. First, if you still have time left, you can choose to click the End Exam button. Lastly, if you have no time left, the exam will automatically end and all your progress will be considered. In my case, I ended it with some minutes left because I took more than 2 hours on that one number (the last number) because I want to get 5/5 and not 4/5. With regards to the first four problems, I finished it less than 1.5 hours feeling satisfied with 5/5. So yes, you will know how good your model so far.

The exam costs $100 and be prepared to ready a valid ID. You can pay for it and take the exam at a later time. Though, beware of the expiration of the access upon payment.You can find the process by clicking the Begin exam button found in the website.

Don’t worry, clicking that button won’t start the clock ticking for the exam because you actually haven’t paid yet. It will redirect you to this:

You can also avail the TensorFlow Education Stipend. This stipend offers various assistance you can take advantage of. The first one is the exam stipend which will give you 50% discount ($50 USD). The other choice is to avail both the stipend for the deeplearning.ai’s TensorFlow Developer Certificate and the exam stipend. You will be able to access all included courses via Coursera. Though, the exam stipend still offers only the 50% discount like the first option.

This sits on top of it all. I can’t emphasize that further. I have read this many times while starting, preparing and even right before the exam. This is like the battle plan being handed to you because it contains the Skills Checklist.

This candidate handbook, along with other materials, is being updated from time to time so make sure you are using the latest version which you can find in their website. I said that because you may find other blogs that actually used the old versions. So as of this writing, this was updated last October 26, 2020. I have read other blogs and so far, the new section that I noticed was the TensorFlow developer skills which is the first section.

If the candidate handbook is your battle plan, this is your battle plan details. Trust me, if you have completed this, have coded this, have religiously studied this or really know this, you will certainly pass the exam.

I second this note written in Coursera:

I keep repeating this because I want to stress that really. If you don’t have much time and you know the concepts of Deep Learning for simple regression, computer vision, natural language processing, and time series and just need to learn the TensorFlow part, take this.

To complete the holy grail of the exam, you must know PyCharm. We do not want to take the exam and be surprised by the environment that will be used. That would be a catastrophe.

Do you need to be a pro using PyCharm? Certainly not needed but no one is stopping you if you want to. I actually haven’t used PyCharm before the exam so I just listed the things that I needed to know. I listed setting up the environment in PyCharm (this is so cool like a built-in venv), knowing how to run a program in it and making myself feel comfortable using it. If you’re a dark mode lover like me, they have cool darcula themes in there too!

For easy install, I installed it using the Ubuntu Software application. As far as I know, any edition(community, pro or edu) will work but please go back to the TensorFlow website to confirm the latest instructions. When I took the exam, I used the PyCharm CE(Community Edition, version 2020.2).

This book is awesome. If you want to learn from classical machine learning to neural networks using one book, spot on, pick this book.

I bought this in Amazon during their sale last year. I was actually using this book for classical machine learning algorithms but for the exam, I only used it as a reference book. I can say, what’s written in the second part of the book covers a lot more than the exam. So if you want to focus on the exam first, you may just want to use this as a supplementary material. Surely, after the exam, if you want to know more, go back to this as your primary material. Definitely worth it. That simply explains the 5 star rating in Amazon.

Given my previous months of learning Deep Learning concepts, I used this only as a review and only picked the relevant stuff.

Been there, done that. Learning and listening to people who have done the exam is such a great thing to do. These are some blog articles that I found really helpful (in no specific order):

2. How to Pass the TensorFlow Developer Certificate Exam by Harshit Tyagi

3. Part II: The TensorFlow Developer Certificate by Viren Radhakrishnan

Apart from this, I also tried to look at different forums and reddit posts. Of course, this should be done ethically, in a way that it just helps you in the preparation but not cheating! In the first place, you’re doing this not to just get the certificate but to challenge yourself and learn.

This is one thing that will increase much of your chances more than anything else. I recommend practicing the exercises and the programming homeworks from the TensorFlow Developer Professional Certificate using Google Colab and PyCharm. I also practiced once more after each course and I practiced all of it again after finishing all of the courses.

Before the exam, I set aside one week to practice again and increase my familiarization with the workflows for the different parts like in Computer Vision, Natural Language Processing, and Time Series. Different data and use cases may arise but it’s one step forward if you understand and know the given use cases in all of those courses. I even made flowcharts for each use case and given different data and see if I can recall it well. All stuff that I used was organized in my github repository below.

It’s not necessary to do it the way I did it. Just have a way to make it stick enough. When I was doing the exam, I thanked myself for preparing that way. It worked!

After you press that End Exam button, you will get a confirmation whether you pass it or not. This image below shows the snapshot of the email I received.

Passing the exam before New Year’s Eve added up to the excitement and the celebration going around. It’s a good year ender and the day after that, I started a new chapter for 2021. As of this writing, I am now working on AI and Computer Vision projects as a hired individual and also as a volunteer to other impact projects. Learning just never stops.

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