Hi everyone,
A few years ago, if someone had told me that I would one day be earning Google Cloud certifications, I probably wouldn’t have believed them.
I come from a non-tech background.
Cloud computing, infrastructure, AI, machine learning — all of these once felt like completely unfamiliar territory. There were no shortcuts for me. I had to start from the fundamentals, understand concepts that initially made very little sense, make mistakes, revisit the same topics again and again, and slowly build confidence.
A few weeks ago, I achieved my Google Cloud Associate Cloud Engineer certification.
That achievement gave me confidence, but I didn’t want to stop there.
I decided to take on another challenge: the Google Cloud Generative AI certification.
And once again, the journey was far from easy.
There were late nights spent studying when I was already exhausted. There were concepts I had to read multiple times before they finally clicked. There were moments when I questioned whether I was preparing enough, whether I should postpone the exam, and whether I was moving too fast after my previous certification.
But one thing I kept reminding myself:
You don’t need to know everything from the beginning. You just need to keep moving forward.
So I continued.
One more topic.
One more practice session.
One more revision.
One more late night.
Instead of focusing on how much I still didn’t know, I focused on learning a little more every day.
Resources I used for my preparation
I didn’t depend on one single resource. I used a combination of documentation, videos, courses, practice questions, and technical articles. Each resource helped me in a different way.
1. Google Cloud Documentation
Google Cloud documentation was one of my most important resources.
Whenever I wanted to understand a service, feature, terminology, use case, limitation, or recommended approach in more depth, I went directly to the official documentation.
I didn’t try to memorize every page. Instead, I used the documentation to strengthen topics where I felt weak and to verify concepts that were unclear from videos or practice questions.
Official documentation also helped me understand the way Google describes its own products and solutions, which is extremely useful when preparing for a Google Cloud certification.
2. Random YouTube Videos
YouTube helped me a lot when I needed a concept explained in a different way.
Sometimes I could read the same topic multiple times and still not completely understand it. Watching someone visually explain the concept often made things click much faster.
I searched for videos around individual topics rather than depending on one particular channel.
If I was struggling with a Generative AI concept, a Google Cloud service, prompt engineering, responsible AI, machine learning terminology, or another exam objective, I would search for that specific topic and watch different explanations until I understood the underlying idea.
3. Udemy Video Course + Practice Test Course
I also used a structured video course on Udemy to go through the certification topics in an organized manner.
The video course helped me build the foundation and gave me a clear learning sequence instead of randomly jumping between topics.
After building that foundation, I used the practice test course by Sayyam on Udemy.
This was particularly useful for testing whether I could actually apply what I had learned.
Instead of simply checking whether my selected answer was correct or incorrect, I carefully studied the explanations for all the answer choices.
For every question I got wrong, I tried to understand:
Why was my selected option incorrect?
Why was the correct option better?
What concept was the question actually testing?
What small detail in the question changed the answer?
This approach helped me identify gaps that passive video learning alone would never have exposed.
4. Practice Questions from KloudExams
I also practiced questions from KloudExams.
For me, practice questions were not about memorizing answers. They were a way to repeatedly expose myself to different scenarios and train myself to identify the important clues inside a question.
Whenever I answered something incorrectly, I went back to the underlying concept and revised it.
I also spent time understanding the incorrect options because knowing why three choices are wrong can sometimes be just as valuable as knowing why one choice is correct.
Repeated practice helped improve my confidence, speed, and ability to eliminate incorrect choices during the actual exam.
5. Google Cloud and Generative AI Technical Blog Posts
I regularly read technical blog posts related to Google Cloud and Generative AI.
Documentation is excellent for understanding how a product works, but blog posts often helped me understand how technologies are actually being used in real-world scenarios.
They were especially helpful for connecting individual concepts together — Generative AI, foundation models, enterprise AI use cases, responsible AI, data, security, and Google Cloud services.
Reading these articles also helped me become more comfortable with the terminology used throughout the Google Cloud AI ecosystem.
6. Repeated Revision
One resource that is easy to underestimate is your own revision.
I kept revisiting topics that I had previously studied.
The first time I studied something, I might understand 50%.
The second time, maybe 70%.
After seeing related practice questions and revisiting the documentation, suddenly the same topic would make much more sense.
That repeated cycle of:
Learn → Practice → Get something wrong → Understand why → Revise → Practice again
was probably one of the most valuable parts of my preparation.
I never relied completely on a single course, a single video, or a single set of practice questions.
I tried to build understanding from multiple sources.
And finally, exam day arrived.
All those hours of preparation, doubts, revisions, mistakes, practice questions, documentation pages, videos, and late nights came down to that one attempt.
And I passed the Google Cloud Generative AI certification on my first attempt. 
For me, this achievement is about much more than adding another certification to my profile.
It is another reminder that:
Your starting point does not decide how far you can go.
Coming from a non-tech background certainly made the journey harder, but it also taught me something incredibly valuable:
Technical skills can be learned.
Difficult concepts can eventually become familiar.
Confidence can be built.
You simply have to be willing to struggle through the stage where everything feels difficult.
If you are currently preparing for a cloud or AI certification and things feel overwhelming, keep going.
You might fail a practice test.
You might spend hours understanding something that someone else seems to understand in minutes.
You might have days when you feel like you are making no progress at all.
That’s okay.
Go back to the documentation.
Watch another explanation.
Read another article.
Solve another question.
Understand another mistake.
And keep moving.
Hard work compounds. Knowledge compounds. Confidence compounds.
A few weeks ago: Google Cloud Associate Cloud Engineer.
Today: Google Cloud Generative AI certified — first attempt.
And the learning continues. 
Never give up just because the journey is difficult. Sometimes the difficult journey is exactly what makes the achievement meaningful.