Microsoft Azure DP-100 Korean

Step 3: Taking Online Learning Paths

In addition, Microsoft recommends that the candidates for the DP-100 exam take online courses to adequately prepare for their test. You can find the following learning paths on the official certification webpage:

• Create Machine Learning Models
• Create No-Code Predictive Models with Azure Machine Learning
• Build AI Solutions with Azure Machine Learning

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

Simulation test system

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Unlimited use

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First-class service

The customer is God. Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) learning tool provide all customers with high quality after-sales service. After your payment is successful, we will dispatch a dedicated IT staff to provide online remote assistance for you to solve problems in the process of download and installation. During your studies, DP-100 Korean study tool will provide you with efficient 24-hour online services. You can email us anytime, anywhere to ask any questions you have about our DP-100 Korean study tool. At the same time, our industry experts will continue to update and supplement DP-100 Korean test question according to changes in the exam outline, so that you can concentrate on completing the review of all exam content without having to pay attention to changes in the outside world.

2. Train Models & Run Experiments (25-30%):

• Metrics generation from experiment runs: The candidates must be able to use logs for troubleshooting errors in experiment runs, log metrics from experiment run, and view and retrieve experiment outputs.
• Model training process automation: The individuals need the relevant skills in running pipelines, passing data within steps in pipelines, monitoring pipeline runs, and creating pipelines with the use of SDK.
• Models creation with Azure ML Designer: This domain covers the examinees’ skills in using custom code modules within the design and using designer modules for the definition of pipeline data flows. It also requires one’s competence in ingesting data within designer pipelines and creating training pipelines utilizing ML Designer.
• Training scripts run within Azure ML workspaces: The students should have the expertise in creating and running experiments utilizing Azure ML SDK as well configuring run settings for the scripts. This subject area also requires their skills in data consumption from datasets for an experiment using Azure ML SDK.

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