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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.
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.
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.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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This is the most recommended way of preparing for the Microsoft DP-100 exam. Microsoft offers the official training course under the title “Designing and Implementing a Data Science Solution on Azure” to help the students build a solid knowledge base for the test. The training sessions are delivered to the individuals by the world-class professionals with plenty of experience in the field. So, the candidates get trained accordingly in the best way possible. Another advantage of taking the instructor-led course is that you have an opportunity to ask your questions and receive feedback whenever you face difficulties.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and prepare a machine learning solution | 20-25% | - Manage compute resources
|
| Topic 2: Optimize language models for AI applications | 25-30% | - Evaluate and improve models
|
| Topic 3: Train and deploy models | 25-30% | - Train models
|
| Topic 4: Explore data and run experiments | 20-25% | - Implement pipelines
|
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