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DP-100 Exam Target Audience
Candidates with permission to take DP-100 exam are those aspiring to become Azure data scientists and have formed huge knowledge in both data science and machine learning workloads based on Azure.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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:
- 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.
- 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.
Microsoft DP-100 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Topic 2: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 3: Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Topic 4: Design and prepare a machine learning solution | - Manage compute and data assets - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads |
| Topic 5: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
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