6 things you need to do if you’ve failed the Google Machine Learning Certification Exam

Paul Kamau
6 min readOct 2, 2022

So we’ve all been there. Something is super important to you, and you pour your heart and soul into it. You go into laser focus mode, work extra hours, hunker down a couple of weekends, skip a few social meets hoping the sacrifice is worth it. And then finally, that day arrives and you fail at what you put your heart and soul into. It sucks, It’s devastating.

My story:

I failed my ML certification back in December 2021.

For the most part, I’ve always been a good test taker and within my first 3 months at Google, I sat for the PCA. And 4 months later, I did the Data Engineering Certification test. I passed both these tests on the first attempt.

The main test that I really cared about was the Machine Learning certification. I sat for it in December 2021, having studied for only 4 months and failed it. Horribly, I’d assume because of a couple of reasons: The first is that I had not fully completed all the study materials (about 60% through) and secondly, the content I did study was not the main focus of the test.

Here are six important important things to remember

  1. Take a break
  2. Remember why you started this journey
  3. You’re not alone. Everyone is trying to figure this out.
  4. Calibrate and focus your learning exclusively on the test syllabus, not Machine Learning as a whole.
  5. Switch things up (Learning materials, study areas, influences)
  6. Set a date and retake the exam

1. Take a break.

So when I was studying for this test, I skipped out on a lot of things. Friends meetup, movie nights, didn’t go out much. I spent most of my time prepping, doing practice tests, writing insanely long cheat sheets and re-watching lectures over and over to try to understand these concepts. So after failing the test, I put everything aside for a whole month and took that vacation I had pushed off. I had booked this prior to my test.

So here’s a tip!

Plan what you’re gonna do BEFORE you take your test, regardless of whether you pass or fail it. Have a trip planned for beautiful France or Mobile, Alabama? Commit to going and having a good time despite the outcome of the test.

Whether it’s getting more sleep, reaching out to friends, or picking up your hobbies, do everything but think about the test. You need to recharge and center yourself again. Honestly, there are better things in life than stressing over a test and missing out on life.

2. Remember why you started this journey

Machine learning is an exciting and rapidly-growing field, with many opportunities for people to learn and build incredible products and services. However, it is not easy. But it’s definitely worth the time and investment put into it to master it as every other industry is pivoting to this area and will be defined by it.

A PwC report estimates that AI will contribute $15.7 trillion to the global economy by 2030. AI will make products and services better, and it’s expected to boost North America’s GDP by 14% by that year. (source)

So don’t you want a slice of that pie?

3. You’re not alone. Everyone is trying to figure this out.

It’s important to have a healthy relationship with failure. If you don’t, then you’ll never pursue risky or difficult challenges. Instead, you’ll focus on the things that come easy for you, and you’ll never really push yourself outside of your comfort zone.

Machine learning is still a field in its infancy. Even though AI has been around since the 1950s, and different practices sprung up in the 60s through the AI winter to now the 2020s, this field is still in its infancy and everyone is still trying to figure out things, make it simpler.

Remember, ML isn’t just the domain for Data Scientist, everyone can win with AI. There are low code services like AutoML provide a point and shoot approach to building ML models, to more sophisticated workflows with TensorFlow and BQML.

So what that means is, there’s a community of people willing to work with and advise you. I love using Instagram’s & YouTube’s AI Community for this connection. Check out Kaggle too.

4. Calibrate and focus your learning exclusively on the test syllabus, not Machine Learning as a whole.

Your approach to the ML Certification shouldn’t be a scorched-earth method, but very surgical in nature. In a previous video, I covered what the certification test objective and expected outcome was. (Check it out)

So here, a quick pause and some self-examination is needed. For example, are you trying to export or bring your software dev methods into ML? Are you attempting to fall back to what you’re comfortable with and is it affecting your learning of a new discipline?

When I was originally preparing for the certification, I was excited to embark on the journey and just wanted to start building and learn as much as I could in GENERAL. Having a software background ( I built over 10 SaaS apps), I assumed the test would be code heavy, and ignored the syllabus framework a little to learn the things which I knew I was bad at and just wanted to understand. I was curious to learn how to use python to build my models, and I did tons of labs and challenges on Kaggle and CloudSkillsBoost to make up for that deficiency. While this is extremely useful as a method of continuous learning, it’s not in scope for the certification. The learning modules definitely include practice labs to go along with the theory taught, but not all of it was required for the ML certification, even though it was useful for the ML practitioner path.

So use the benefit of hindsight and set a primary focus on what is on the syllabus and a secondary one on everything else that follows.

5. Switch things up (Learning materials, study areas, influences)

Don’t hesitate to change learning courses / instructors, resources and experiment with new approaches.

I have at least 6 subscriptions across Pluralsight, Brilliant.org, CloudSkillsBoost, Kindle Unlimited, and that’s just a brief mention. Therefore, if you’re getting bored with a particular instructor, course, book or material, change it!

The field of ML is like Machine Learning is a raging fire. So if the learning resources aren’t stoking your flame, change it up. Being able to make a strategic pivot is one of the most valuable skill sets to have in the technology field.

6. Set a date and retake the exam

Lastly, take the dive and schedule your next exam date. This was the first thing I did after failing my test the first time. In my experience, having a set date to work towards a specific goal. It ensures that you’re disciplined enough to use your time well.

I retook the test on April 28th 2022 and passed my certification. And man it was the best feeling ever. I told everyone. And some of you know what that’s like. To see your hard work pay off in such a massive way. I stopped to smell the roses, took a vacation and began making plans on what to do next. But that’s a video for another time.


What comes after? Check out my next blog… I’ve passed my Machine Learning Certification. What’s next?


So that was my journey. Do you have a similar story? What are your tips for dealing with failure and what’s worked for you? Let me know below and also if you have any questions, drop it in the comments below.

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I share early career advice, mental health tips, tutorials on BQML, AutoML,TensorFlow, interview tips & regular Q&As 🚀



Paul Kamau

TAM @google | I write about tips, tutorials and impact AI & Machine Learning | https://bio.link/paulkamau