For the Microsoft DP-100 Exam, titled "Designing and Implementing a Data Science Solution on Azure," candidates should possess expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. The exam is designed for data scientists who use Azure Machine Learning and MLflow to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring.
Audience:
- Data Scientists: Professionals who build and deploy machine learning models.
- Machine Learning Engineers: Individuals focused on operationalizing machine learning models.
- Data Engineers: Those involved in preparing data and managing data pipelines.
- IT Professionals: Technologists looking to enhance their skills in Azure’s machine learning services.
- Aspiring Data Scientists: Newcomers to the field seeking to validate their Azure data science skills.
Key areas covered in the exam include:
- Designing and preparing a machine-learning solution
- Exploring data and training models
- Preparing a model for deployment
- Deploying and retraining a model
Benefits of Certification:
Achieving the Microsoft Certified Azure Data Scientist Associate (DP-100) credential offers numerous benefits. It validates your expertise in deploying and managing machine learning solutions using Azure technologies. This recognition not only sets you apart in the competitive job market but also opens doors to diverse career opportunities within data science and machine learning domains. Employers prioritize certified professionals, giving you a competitive edge and potentially higher earning potential. DP-100 certification signifies your commitment to continuous learning and professional growth, providing access to exclusive resources and a supportive community. Overall, it solidifies your value to employers and boosts confidence in tackling real-world data challenges effectively.
Candidates will be evaluated on their ability to design a suitable working environment for data science workloads, explore data, train machine learning models, implement pipelines, run jobs for production, and manage, deploy, and monitor scalable machine learning solutions.
