Microsoft Certified Azure Data Scientist Associate

Interested in Data Science? Get certified as Microsoft Azure Data Scientist Associate and advance your career.

The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

The average salary for a Microsoft Certified Azure Data Scientist Associate is $77,182.

About Microsoft Certified Azure Data Scientist Associate

The Azure Data Scientist Associate certification follows Microsoft’s departure from more broad certifications like the Microsoft Certified Systems Administrator (MCSA) or its older sibling, the Microsoft Certified Systems Engineer (MCSE). Nowadays the focus is on specific roles. Please note that this exam is currently in beta.

As the name suggests, this certification has “data science” written all over it, specifically Azure data science offerings and features. Since it is an Associate-level certification, the required exam covers a wide range of data science topics and technologies.

Exam DP-100: Designing and Implementing a Data Science Solution on Azure – Skills Measured

This exam measures your ability to accomplish the following technical tasks: set up an Azure Machine Learning workspace; run experiments and train models; optimize and manage models; and deploy and consume models.

Set up an Azure Machine Learning Workspace (30-35%) 

  • Create an Azure Machine Learning workspace 
  • Manage data objects in an Azure Machine Learning workspace 
  • Manage experiment compute contexts 

Run Experiments and Train Models (25-30%) 

  • Create models by using Azure Machine Learning Designer 
  • Run training scripts in an Azure Machine Learning workspace 
  • Generate metrics from an experiment run 
  • Automate the model training process 

Optimize and Manage Models (20-25%) 

  • Use Automated ML to create optimal models 
  • Use Hyperdrive to tune hyperparameters 
  • Use model explainers to interpret models 
  • Manage models 

Deploy and Consume Models (20-25%) 

  • Create production compute targets 
  • Deploy a model as a service 
  • Create a pipeline for batch inferencing 
  • Publish a designer pipeline as a web service 

Get Certified

You may also like: Google’s Professional Machine Learning Engineer Certificate


 

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