So recently on 30th October 2022, I passed my Tensorflow Certification Exam and in this blog, I'll share my entire journey from preparation to getting certified, starting with

Resources I used to prepare for the exam:

1. Deep learning Specialization (Andrew Ng) - Coursera
2. TensorFlow Developer Certificate in 2022 - Zero To Mastery
3. DeepLearning.AI TensorFlow Developer Professional Certificate

also, some internships did help me boost my skills in the optimization part.

Of course, you are not limited to only these resources. It's just that these helped me and something else might work for you.

Actually, my entire certification exam was sponsored by Google as I was a part of Google Machine Learning Bootcamp 2022 Cohort 1 and I was provided with a coupon to undertake the examination.

After you are confident enough that you are ready for the exam, you can appear the exam using this link.

But before that, here are some things you need to know about the exam:

1. Go through the TensorFlow Developer Certificate Candidate Handbook to clear most of the queries related to the exam.

1. It is advisable to sign up 2 hours before the exam as the ID verification might take up to 5 minutes to 2 hours to verify. You must have any of the following IDs for ID verification

   • Passport
   • Voting Id
   • Driving License
   • Identity Card

   It is usually an automated process where they'll tell you within 5 mins whether your id is verified or not, and if not it'll take about 2 hours to verify the process by their admin manually.

2. During filling out your personal details even though if you have the coupon code you still have to enter your billing details and afterwards it'll just say that amount is $0 so you have nothing to pay.

   

3. After payment is done then you get an option saying to redeem the exam. You redeem it and a start option will pop up then you can start your exam.

   

Things you need to know to start the exam:

1. You need to get familiar with Pycharm IDE

2. You to install the "TensorFlow Certification Exam" Plugin and this link will explain the steps to install the plugin and how to start the exam

Things you need to know after you start the exam:

1. As shown above in the image you'll be provided with 5 categories with each category containing 1 question.

   The categories may consist of 5 types:

   1. Neural Networks (Classification/Regression)
   2. Image Classification (Binary/ Multiclass)
   3. Computer vision with CNNs
   4. NLP
   5. Time series, Sequences, and Prediction

2. Model training can be done using Pycharm and mostly the dataset for these questions are not huge and are probably compatible to train on your local device but still if your PC doesn't have enough computational power to train the model then you can train your model on the colab and download the model in "modelname.h5" format and paste it in the respected category folder. They don't check the code but only evaluate the saved model on their private test cases.

3. There are 5 questions in this exam with increasing difficulty from 1-5. Please note that the weight of the grade for the question is relative to its difficulty. So your Category 1 question will score significantly less than your Category 5 question.

4. After the model is trained and saved, you can test and submit the model using the Assistant provided on the right side of Pycharm. (Note: You can test and submit your model multiple times and there is no penalty for that so you can test it multiple times until you score 5/5)

   

5. It is a 5-hour long exam and you need to score above 90% to pass the exam. As the weightage differs according to the difficulty it is advisable to score 5/5 on each question.

Types of questions I received:

Note: To preserve the confidentiality and excitement of the exam these questions won't be exactly same but similar to this

1. Getting Started Question

   A basic regression question dataset will be given and we have to train a neural network to match the x to the y.

2. Basic Datasets Question

   Create and train a classifier for the Tensorflow Image dataset. Note that the test will expect it to classify n classes and that the input shape should be the native size of the TF dataset which is NxN monochrome. Do not resize the data. Your input layer should accept NxN as the input shape only. If you amend this, the tests will fail.

3. Computer vision with CNNs

   Create and train a classifier for CV Image Dataset using the provided data. The test will use images that are NxN with N bytes color depth so be sure to design your neural network accordingly

4. NLP QUESTION

   Build and train a classifier for the NLP Dataset. It will be tested against a number of sentences that the network hasn't previously seen and you will be scored on whether prediction was correctly detected in those sentences.

5. TIME SERIES QUESTION

   Build and train a neural network to predict the time-indexed variable of the dataset_name Using a window of past N observations of N feature, train the model to predict the next N observations of that feature.

Things you need to know after you End the exam:

1. You will have an End test option on the top right side of pycharm.

   

2. After ending, immediately a mail will be sent saying whether you are pass or not(No marks are shown)

   

3. If you pass, another Tensorflow certification mail will be sent along with your certificate and badge that you have passed the Tensorflow Developer Exam.

   

4. Also google forms will be sent to add your name to the Tensorflow Network where recruiters will find out a Tensorflow developer in a particular region.

My personal experience and tips to qualify for the exam

To be honest I faced multiple difficulties during the exam I was completely unaware about the fact that how the exam is going to be for example details about plugins and Assistant. So to help people to overcome these problems this blog is going to help you a lot to get an overview of exam.

Some of those difficulties were that my pycharm was not compatible with the TF Developer Exam Plugin so I had to reinstall the entire setup.

Also, the virtual environment that I was provided was causing multiple module errors which it wasted my 30mins and I used another interpreter for the exam. (Note: Though you only need a saved model to pass the test and not pycharm code so even if you couldn't do it, you can download the model from colab)

For me, the exam was completed in the first 2-hours itself but it took me 3-hours to optimize the code as I scored pretty terribly like 2/5 and 3/5 in the first tries, and after few hours of optimization I went up to 5/5

My scores were:

• Question 1: 5/5
• Question 2: 5/5
• Question 3: 5/5
• Question 4: 4/5
• Question 5: 5/5

And at last, when I passed the exam it felt like I was at the top of the world and it's just the beginning 💯. Every bit of hard work and time I invested with consistency was totally worth the satisfaction I felt at last☺️.

Tips:

• I would rate the exam somewhere between medium and hard.
• It is not about building the entire model as most of the time the entire model will be already present. You just have to write the code between sequential, compiling, and fitting the model.
• Focus more on the optimization of code as it is only the part where you score.