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:
- 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.
- Prepare Data for Modeling (25%)
This subject area revolves around cleansing and transforming data; performing EDA (Exploratory Data Analysis); transforming data into usable datasets.
- 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.
Who should take the DP-100 exam
The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification is an internationally-recognized validation that identifies persons who earn it as possessing skilled as a Microsoft Certified Azure Data Scientist Associate. If candidates want significant improvement in career growth needs enhanced knowledge, skills, and talents. The Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam certification provides proof of this advanced knowledge and skill. If a candidate has knowledge of associated technologies and skills that are required to pass Designing and Implementing a Data Science Solution on Azure (beta) DP-100 Exam then he should take this exam.
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Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Manage compute and data assets - Plan and configure Azure Machine Learning workspace |
| Topic 2: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Topic 3: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 4: Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Topic 5: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
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