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The dream of becoming a highly skilled data scientist can turn into a reality with the help of the Microsoft DP-100 exam. This exam tries to impart an associate-level understanding of data science and machine learning with an aim to generate a skilled workforce of data scientists.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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To nail DP-100, you will need to scrutinize the below-mentioned areas:
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.
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.
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.
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.
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| Section | Objectives |
|---|---|
| Topic 1: Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Topic 2: Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Topic 3: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Topic 4: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 5: Design and prepare a machine learning solution | - Plan and configure Azure Machine Learning workspace - Manage compute and data assets - Select appropriate Azure services for machine learning workloads |
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