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Microsoft DP-100 exam covers a range of topics related to data science, including understanding business requirements, data exploration and preparation, modeling, training and evaluation of machine learning models, and deployment and management of models. DP-100 exam also covers topics such as feature engineering, model interpretability, and automation of machine learning workflows. DP-100 exam is designed to test the candidate's ability to design and implement end-to-end machine learning solutions that meet business requirements and are scalable, reliable, and secure.
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The DP-100 exam covers a range of topics, including data exploration and transformation, machine learning modeling, and deployment and management of machine learning models on Azure. DP-100 exam also focuses on the candidate's ability to work with Azure services such as Azure Machine Learning Studio, Azure Databricks, and Azure Cognitive Services. Successful candidates will have a deep understanding of the Azure platform and its capabilities, as well as the ability to design and implement data science solutions that meet business requirements.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Microsoft DP-100 certification exam is an excellent way for data professionals to demonstrate their expertise in designing and implementing data science solutions on the Azure platform. By passing DP-100 exam, data professionals can greatly enhance their career prospects and demonstrate their proficiency in working with big data and machine learning on the Azure platform. If you are interested in pursuing a career in data science, this certification is definitely worth considering.
Microsoft DP-100 exam is a challenging certification exam that requires a thorough understanding of data science and machine learning concepts. It is an excellent way for professionals to demonstrate their skills and knowledge in the field of data science and machine learning and gain a competitive edge in the job market. Passing DP-100 exam will provide individuals with a valuable certification that is recognized by employers worldwide and open up new opportunities for career growth and advancement.
| Section | Objectives |
|---|---|
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Design and prepare a machine learning solution | - Manage compute and data assets - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads |
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
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