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To nail DP-100, you will need to scrutinize the below-mentioned areas:
The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.
Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.
The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.
This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Explore data and train models | 35-40% | - Prepare data for modeling
|
| Topic 2: Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Topic 3: Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Topic 4: Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
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