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:
- Build AI Solutions with Azure Machine Learning
- Create No-Code Predictive Models with Azure Machine Learning
- Create Machine Learning Models
Microsoft DP-100 certification exam is an excellent way for data science professionals to validate their expertise in designing and implementing data science solutions on Microsoft Azure. DP-100 exam is recognized globally and is highly regarded by employers in the field of data science. Designing and Implementing a Data Science Solution on Azure certification is a testament to the candidate's knowledge and expertise in the field of data science and can open up new career opportunities.
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The Microsoft DP-100 exam covers a range of topics, including data preparation, data modeling, machine learning, and data visualization. Candidates are expected to have a strong understanding of these concepts and be able to apply them in real-world scenarios. DP-100 exam also evaluates the candidate's ability to work with Azure services such as Azure Machine Learning, Azure Databricks, and Azure Synapse Analytics. Passing the Microsoft DP-100 certification exam is a great way to demonstrate your expertise in data science and increase your career opportunities in this field.
The DP-100 certification is a great way to showcase your proficiency in data science and Azure solutions to potential employers. It demonstrates your ability to design and implement data science solutions using Azure technologies, which are widely used in today's industry. Moreover, being certified can also help you stand out from the competition and boost your career prospects.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| 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 |
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
