Are you looking to get certified in either of:
- AI-100: Designing and Implementing an Azure AI Solution (or)
- DP-100: Designing and Implementing a Data Science Solution
But not able to make up your mind as to which one you should do? Confused about the two exams, as they sound pretty much similar?
Then, this post is for you. Read on!
First, have a look at this image below. This should clear up the majority of your doubts.
AI-100 vs DP-100
The AI suite of tools from Microsoft can be classified into 3 categories:
- Azure Cognitive Services
- Azure Bot Service
- Azure Machine Learning
Simply put, the focus of AI-100 will be Azure Cognitive Services and Azure Bot Service. And, for DP-100, it will be Azure Machine Learning and other related tools
The objectives for each of the exams are based on the above set of AI services from Azure.
Azure Cognitive Services (AI-100)
Azure Cognitive Services contain a comprehensive set of tools to apply AI to your products.
AI Engineers work with data scientists & data engineers to build apps that use Microsoft Azure Cognitive Services like Azure Search, Language, Speech & Vision to solve problems like understanding emotions in text, recognizing faces, etc.
As part of the exam, you will be required to understand when a custom API should be developed to meet project requirements.
Azure Bot Service (AI-100)
For this part of the exam, you will be required to build and integrate Bots with other Azure services like Azure Application Insights and Azure App Services.
For AI-100, you would not be required to build/train Machine Learning models from scratch. You need to know how to use the pre-built Azure AI in your application.
AI-100 exam is targeted towards AI Engineers
The AI-100 Study Guide will help you to prepare for the exam.
You do not need machine learning or data science knowledge.
Azure Machine Learning (DP-100)
For DP-100, you learn to create experiments that train a machine learning model from raw data. So, you build machine learning models from scratch.
So, concepts like performing Exploratory Data Analysis (EDA), data cleaning, data transformation, feature extraction, & feature selection are important.
You create experiments either in Azure Machine Learning Studio (if you are getting started) or Azure Machine Learning Service (for experienced data scientists)
This exam requires knowledge of machine learning or data science.
DP-100 is targeted towards Data Scientists
The DP-100 Study Guide will help you to prepare for the exam
How AI-100 Is Different from DP-100?
For the AI-100 exam, you focus on working with pre-trained models (where data is already trained on algorithms and available as a REST API) and call their APIs to add cognitive abilities for your application.
With DP-100, you would go through the entire process of building models from scratch. You would collect data, clean data, transform data, select algorithms, train models, and deploy them.
You take the longer route. The final output is the same.
If you are preparing for DP-100, you can build models for very specific problems. But, it requires a lot of domain knowledge and understanding of the subject matter of the problem at hand.
Which Certification Is Right for Me? AI-100 or DP-100?
For both the exams, the objective will be to add AI to your products, businesses, etc.
- AI-100 is for developers without any Machine Learning experience
- DP-100 is targeted for data scientists.
So, choose the exam based on your current role/role that you see yourself in the future.
Preparing for the Exam
Now that you are clear which certification exam to appear for, it is time to prepare for the exam. Here are some resources:
Are you preparing for either AI-100 or DP-100 exam? Let me know if I can help you in any way in the comments section. Also, I love to hear from you about how your preparation is going on!
Follow/Like ravikirans.com to Receive Updates
Want to be notified as soon as I post? Subscribe to RSS feed / leave your email address in the subscribe section. Share the article to your social networks with the below links so it can benefit others.
Share the Article in Your Social Media Networks