Step 4: Training with Practice Tests
Practicing more and more before taking the exam can help you score better. Using practice tests, you can also significantly improve your time management skills. You can easily find the relevant practice questions on different online learning platforms.
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The dream of becoming a highly skilled data scientist can turn into a reality with the help of the Microsoft DP-100 exam. This exam tries to impart an associate-level understanding of data science and machine learning with an aim to generate a skilled workforce of data scientists.
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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:
- 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.
- 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.
- Prepare Data for Modeling (25%)
This subject area revolves around cleansing and transforming data; performing EDA (Exploratory Data Analysis); transforming data into usable datasets.
Microsoft DP-100: Requirements
The Microsoft DP-100 exam is created for the individuals who are involved in implementing machine learning techniques. The candidates for this certification test should have an in-depth knowledge of identifying and preparing the development environment, applying scientific data and rigor exploration techniques to acquire actionable insights along with communicating results to the stakeholders. They should also possess the skills in preparing data for modeling and developing models. In addition, they should also be able to apply machine learning techniques to train, deploy and evaluate models to build any AI solutions, which satisfy the business objectives.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Simulation for the App version
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Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Design and prepare a machine learning solution | 20-25% | - Manage data assets
|
| Explore data and run experiments | 20-25% | - Implement pipelines
|
| Optimize language models for AI applications | 25-30% | - Optimize with Retrieval Augmented Generation
|
| Train and deploy models | 25-30% | - Train models
|






