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Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.

Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. In addition, you manage, optimize, and deploy machine learning models into production.

A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.

Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate

Download exam skills outline

Microsoft DP-100 Korean Exam Syllabus Topics:

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

DP-100 Exam Outline

The Microsoft DP-100 was recently renewed to meet the most current market needs and now it measures the following skills:

• Running Experiments and Training Models.
• Deploying and Consuming Models;
• Setting Up the Workspace for Azure Machine Learning;
• Optimizing and Managing Models;

The DP-100 exam domain of Setting Up the Workspace for Azure Machine Learning (ML) has three sections. The first touches on creating the workspace for ML. Here, you're to come across tasks like creating and configuring the workspace and managing it using Azure ML studio. The next part is concerning data object management within the workspace of Azure ML, where the focus goes to registering and maintaining datasets. The final aspect regards maintaining contexts for experiment compute. Under this, there will be creating instances for compute, determining the appropriate specs for compute targeting workload training, and developing targets for compute directed at experiments as well as training.

Regarding Optimizing and Managing Models, candidates will build their skills in five crucial areas. To begin is the area of creating optimal models using automated ML. This takes into account areas like Azure ML studio, Azure ML SDK, scaling options for pre-processing, algorithm determination, and getting data to be utilized in running the automated ML. The next thing goes into tuning hyperparameters using hyperdrive. Candidates need to note the sampling methods, search space, primary metric, termination options, and the right model. Another field concerns managing models where coverage includes model interpreters and feature importance data. Finally, students will learn how to manage models by exploring trained model registration, monitoring model usage, and monitoring data drift.

The Microsoft DP-100 exam also deals with the Deploying and Consuming Models. Of interest, there are four sections. It starts with the creation of targets for production compute involving security meant for deployed services & compute options targeting deployment. It's followed by the part of deploying a model as a service. This touches deployment settings, consuming deployed services, and troubleshooting issues for deployment containers. The next segment is creating a batch interference pipeline. Finally, students look at publishing a web service in the form of a designer pipeline. Issues also covered are compute resource, inference pipeline, and consumption of an already deployed endpoint.

The last DP-100 exam domain talks about Running Experiments and Training Models. The first way to achieve abilities in this area is by learning how to use Azure ML Designer to create models. This will be actualized by exploring creation of a training pipeline, ingestion of data within a designer pipeline, defining data flow for a pipeline using designer modules, and using modules for custom code. The second one regards running training scripts within the Azure ML workspace. Within this sphere, the students' focus will be how to use the Azure ML SDK in consuming data from a dataset in an experiment. The third thing in this topic has to do with using an experiment run to generate metrics. Here, learning includes log metrics, retrieving and viewing experiment outputs, and troubleshooting experiment errors using logs. The fourth and final area of concern is automating the process of model training. This includes developing a pipeline by utilizing the SDK, passing data, running a pipeline, and monitoring pipeline runs.

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Following are some reference material for actual Microsoft DP-100 Korean exam test

Step 5: Studying with Books

Books is another efficient preparation tool. There are several guides available for the Microsoft DP-100 exam but the most popular are listed below:

• Learn How to Pass DP-100: Designing and Implementing a Data Science Solution on Azure Exam: Learn at your fingertips by Rodrezil Publications
• Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure 1st Edition by Pierstefano Tucci
• Designing and Implementing a Data Science Solution on Azure - Exam Prep: Microsoft Azure DP-100 Certification Exam | Most Unique & Latest Questionnaires by VB Dev
• How I Passed DP-100: Designing and Implementing a Data Science Solution on Azure: Sure Shot Certification Tips by Empirical Matt Publications
• Designing and Implementing a Data Science Solution on Azure: Prepare for exam DP-100 by Stavros Koureas