- Exam Code: DP-100
- Exam Name: Designing and Implementing a Data Science Solution on Azure
- Certification Provider: Microsoft
- Corresponding Certification:Microsoft Azure
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Microsoft DP-100: Skills Measured
Microsoft provides you with the elaborate outline of the skills that you need to acquire before attempting the test. The specific topics of the exam along with the main subtopics are enumerated below:
- Prepare Data for Modeling (25%)
This subject area revolves around cleansing and transforming data; performing EDA (Exploratory Data Analysis); transforming data into usable datasets.
- Define and Prepare the Development Environment (15%)
To answer the questions within this objective, the applicants should have the professional ability to accomplish such technical tasks as selecting a development environment; quantifying the business problem; setting up a development environment; etc.
- Develop Models (40%)
The skills measured within this topic include selecting an algorithmic approach; evaluating model performance; training the model; identifying data imbalances; splitting datasets, and so on.
- Perform Feature Engineering (15%)
Answering the questions that are drawn from this domain, the test takers should be able to perform the tasks such as performing feature selection as well as performing feature extraction.
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How much DP-100 Exam Cost
The price of the DP-100 exam is $165 USD.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace - Manage compute and data assets |
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