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The candidates for this Microsoft exam are Azure Data Scientists. These professionals have expertise in applying their knowledge of machine learning and data science to run and implement ML workloads on Azure. This is particularly in the usage of Azure ML Service. These applicants are the experts in planning and creating the appropriate working environments for data science workloads within Azure. They also train predictive models and run data experiments. The individuals who want to earn ACE college credit can also take this certification test.
The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure test has no official requirement. However, the candidates must develop an in-depth understanding of the exam topics. They should also have expertise in model optimization and management and ML models deployment within the production.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
The Microsoft DP-100 exam covers a range of topics, including data preparation, data modeling, machine learning, and data visualization. Candidates are expected to have a strong understanding of these concepts and be able to apply them in real-world scenarios. DP-100 exam also evaluates the candidate's ability to work with Azure services such as Azure Machine Learning, Azure Databricks, and Azure Synapse Analytics. Passing the Microsoft DP-100 certification exam is a great way to demonstrate your expertise in data science and increase your career opportunities in this field.
The certification obtained from passing DP-100 exam is proof of intermediate skills in data science since it is within the associate hierarchy. Apart from applying for and getting a job in a preferred company, you can also expand your skills in an area of interest. With the role-based scheme, there are expert certifications awaiting you to earn. They include the Microsoft Certified: Azure Solutions Architect Expert and the Microsoft Certified: Azure DevOps Engineer Expert.
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| Section | Weight | Objectives |
|---|---|---|
| Explore data and train models | 35-40% | - Prepare data for modeling
|
| Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
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