After publishing parts of my DP-100 learning journey, I received a surprisingly common question:
“Why DP-100?”
It’s a fair question.
The certification is being retired, Azure AI is receiving most of the attention today, and many people are focusing directly on Generative AI.
So why did I decide to invest months of my time preparing for DP-100?
The answer starts much earlier than the certification itself.
It Started with Data
For years, my professional focus has been data.
Power BI became one of my favorite tools because it sits at the intersection of technology and business. I enjoy transforming raw information into something people can actually use to make decisions.
Over time, I became increasingly interested in what happens before the reporting stage.
How is the data generated?
Can we predict future outcomes?
Can systems learn patterns that humans might miss?
Those questions naturally led me toward Machine Learning.
The Kürt Academy Experience
A major turning point was attending the Data Science program at Kürt Academy.
The program introduced me to different types of Machine Learning models, practical use cases, and the thought process behind building predictive systems.
What I enjoyed most was that the instructors were not teaching theory in isolation.
They were working in the field.
They shared real-world examples, real business problems, and real implementation challenges.
For the first time, Machine Learning stopped feeling like an academic topic and started feeling like something I could actually build and use.
I loved it.
And I still do.
The Next Logical Step
At the same time, I was continuing my journey in Microsoft’s ecosystem.
I already held the Microsoft Certified: Power BI Data Analyst Associate certification.
The next question became:
What should I learn next?
I wanted something that would:
- expand my technical knowledge
- build on my existing data background
- remain relevant in enterprise environments
- support my long-term career goals
DP-100 checked every box.
The certification covers the lifecycle of Machine Learning solutions in Azure, including:
- data preparation
- experimentation
- model training
- model evaluation
- deployment
- monitoring
- MLOps practices
It focuses on turning Machine Learning into an operational capability rather than simply building a model once and forgetting about it.
That perspective appealed to me immediately.
More Than an Exam
For me, DP-100 was never just about passing a certification exam.
It was about understanding how Machine Learning works in real organizations.
How models move from an experiment notebook into production.
How teams manage versioning, deployment, monitoring, and continuous improvement.
How data science becomes engineering.
Those concepts remain valuable regardless of which tools become popular next year.
Looking Back
Today, Azure AI is evolving rapidly.
Generative AI is changing the industry.
New certifications are replacing older ones.
That is normal.
Technology moves forward.
But I do not regret choosing DP-100 for a second.
It gave me a deeper understanding of Machine Learning systems, cloud-based deployment, and the operational side of AI.
Most importantly, it helped me build a stronger foundation for everything that came afterward.
Including AI-300.
