As I mentioned in my previous post, my daily routine changed completely.
The comfortable timeline I had originally planned no longer existed.
I had entered a season of focus.
The reason was simple.
DP-100 was approaching retirement.
The final exam availability was scheduled to end on June 2, 2026.
At the same time, I needed to request a certification voucher through my employer, which could take around a week to arrive. Even after receiving the voucher, I still needed to find an available exam slot in my time zone.
And as the retirement date approached, demand was naturally increasing.
I couldn’t assume I would simply book an exam for the next day whenever I felt ready.
Suddenly, time mattered.
So I made a decision.
My daughter was preparing for her advanced Digital Culture graduation exam.
Why not align our goals?
Why not give ourselves a shared finish line?
I chose May 18, 2026 as my target date.
At least, that was the plan.
Unfortunately, by the time I was ready to schedule the exam, the only available slot on that day started at 10 PM local time.
Now, many things can be said about me.
But being at my absolute best during a two-hour professional certification exam at 10 PM is probably not one of them.
So I moved the exam by one day:
May 19, 2026. 3:00 PM.
A much better choice.
Enough time in the morning to review difficult topics.
Enough time to prepare my workspace.
Enough time to test my equipment.
Enough time to have breakfast, calm down, and mentally switch into exam mode.
And still early enough that I could expect my brain to cooperate.
With the date finally fixed, something interesting happened.
The preparation became real.
Every day started to look similar.
Work.
Study.
Practice questions.
Flashcards.
Microsoft Learn.
Repeat.
Seven days a week.
Not because somebody forced me to.
Because I genuinely wanted it.
By this point, I had already invested years into working with data.
Power BI.
Reporting.
Automation.
Process improvement.
Over time, I realized that machine learning felt like the natural next step.
I had already been exposed to machine learning concepts before.
The topic fascinated me.
Last year, I joined the Data Science program at Kürt Academy, where we explored different machine learning approaches, model types, and real-world use cases.
What made the experience memorable was not the theory itself.
It was the instructors.
These were professionals working in the field every day.
Instead of teaching abstract concepts, they showed us how machine learning solves actual business problems.
The possibilities felt endless.
I loved it then.
I still love it today.
That experience made my next learning goal obvious.
Machine learning.
The question was simply how to pursue it.
DP-100 turned out to be the perfect fit.
Not because I wanted another certification badge.
But because it provided a structured path through an area I genuinely wanted to understand.
Many people assume DP-100 is simply about building machine learning models.
In reality, it covers something much broader.
The entire machine learning lifecycle on Azure.
Data preparation.
Experimentation.
Model training.
Evaluation.
Deployment.
Monitoring.
Automation.
MLOps concepts.
Responsible AI practices.
It is not just about creating a model.
It is about understanding how machine learning solutions operate in real-world environments.
Especially in enterprise settings.
The more I studied, the more I realized that this was exactly the kind of knowledge I wanted to build.
And yet, there was another interesting reality.
While the technology continues to evolve rapidly, many organizations are still only beginning their machine learning journey.
Some are still working through reporting modernization.
Some are still discovering the value of automation.
Some have not yet reached the point where machine learning is part of everyday decision-making.
And that’s perfectly normal.
Technology adoption rarely happens at the same speed everywhere.
Which is why I never viewed DP-100 as “old knowledge.”
I viewed it as foundational knowledge.
The kind of foundation that helps you understand what comes next.
And what comes next was already visible.
Generative AI.
Agentic systems.
New Azure AI certifications.
New tools.
New approaches.
The industry was moving forward.
But before I could think about the next chapter, I had one goal.
May 19, 2026.
The exam date was on the calendar.
The preparation phase was over.
Now it was time to find out whether all those study hours would be enough.
