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Microsoft DP-100: Career Prospects
After successfully passing the Microsoft DP-100 exam, you will obtain the Microsoft Certified: Azure Data Scientist Associate certification. Getting certified will allow you to qualify for several positions, including the following titles:
- Administrative Analyst
- Software Developer
- Data Analyst
- Data Engineer
Obtaining this certification is also beneficial from a financial point of view. In fact, the average salary that a certified professional can earn is $96,642 per year.
Skills Covered
To nail DP-100, you will need to scrutinize the below-mentioned areas:
- Deploy and Consume Models
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.
- Set up Azure ML Workspace
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.
- Execute Experiments & Train Models
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.
- Manage and Optimize Models
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.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?
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Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Design and prepare a machine learning solution | 20-25% | - Manage data assets
|
| Explore data and run experiments | 20-25% | - Run experiments
|
| Train and deploy models | 25-30% | - Monitor and maintain models
|
| Optimize language models for AI applications | 25-30% | - Implement generative AI solutions
|
