Designing and Implementing a Data Science Solution on Azure (DP-100日本語版): DP-100 Exam
"Designing and Implementing a Data Science Solution on Azure (DP-100日本語版)", also known as DP-100 exam, is a Microsoft Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 528 Q&As to your DP-100 Exam preparation. In the DP-100 exam resources, you will cover every field and category in Microsoft Azure Certification helping to ready you for your successful Microsoft Certification.
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Microsoft DP-100 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Manage Azure resources for machine learning (25-30%) | |
| Create an Azure Machine Learning workspace | - create an Azure Machine Learning workspace - configure workspace settings - manage a workspace by using Azure Machine Learning studio |
| Manage data in an Azure Machine Learning workspace | - select Azure storage resources - register and maintain datastores - create and manage datasets |
| Manage compute for experiments in Azure Machine Learning | - determine the appropriate compute specifications for a training workload - create compute targets for experiments and training - configure Attached Compute resources including Azure Databricks - monitor compute utilization |
| Implement security and access control in Azure Machine Learning | - determine access requirements and map requirements to built-in roles - create custom roles - manage role membership - manage credentials by using Azure Key Vault |
| Set up an Azure Machine Learning development environment | - create compute instances - share compute instances - access Azure Machine Learning workspaces from other development environments |
| Set up an Azure Databricks workspace | - create an Azure Databricks workspace - create an Azure Databricks cluster - create and run notebooks in Azure Databricks - link and Azure Databricks workspace to an Azure Machine Learning workspace |
Run Experiments and Train Models (20-25%) | |
| Create models by using the Azure Machine Learning Designer | - create a training pipeline by using Azure Machine Learning designer - ingest data in a designer pipeline - use designer modules to define a pipeline data flow - use custom code modules in designer |
| Run model training scripts | - create and run an experiment by using the Azure Machine Learning SDK - configure run settings for a script - consume data from a dataset in an experiment by using the Azure Machine Learning SDK - run a training script on Azure Databricks compute - run code to train a model in an Azure Databricks notebook |
| Generate metrics from an experiment run | - log metrics from an experiment run - retrieve and view experiment outputs - use logs to troubleshoot experiment run errors - use MLflow to track experiments - track experiments running in Azure Databricks |
| Use Automated Machine Learning to create optimal models | - use the Automated ML interface in Azure Machine Learning studio - use Automated ML from the Azure Machine Learning SDK - select pre-processing options - select the algorithms to be searched - define a primary metric - get data for an Automated ML run - retrieve the best model |
| Tune hyperparameters with Azure Machine Learning | - select a sampling method - define the search space - define the primary metric - define early termination options - find the model that has optimal hyperparameter values |
Deploy and operationalize machine learning solutions (35-40%) | |
| Select compute for model deployment | - consider security for deployed services - evaluate compute options for deployment |
| Deploy a model as a service | - configure deployment settings - deploy a registered model - deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint - consume a deployed service - troubleshoot deployment container issues |
| Manage models in Azure Machine Learning | - register a trained model - monitor model usage - monitor data drift |
| Create an Azure Machine Learning pipeline for batch inferencing | - configure a ParallelRunStep - configure compute for a batch inferencing pipeline - publish a batch inferencing pipeline - run a batch inferencing pipeline and obtain outputs - obtain outputs from a ParallelRunStep |
| Publish an Azure Machine Learning designer pipeline as a web service | - create a target compute resource - configure an Inference pipeline - consume a deployed endpoint |
| Implement pipelines by using the Azure Machine Learning SDK | - create a pipeline - pass data between steps in a pipeline - run a pipeline - monitor pipeline runs |
| Apply ML Ops practices | - trigger an Azure Machine Learning pipeline from Azure DevOps - automate model retraining based on new data additions or data changes - refactor notebooks into scripts - implement source control for scripts |
Implement Responsible ML (5-10%) | |
| Use model explainers to interpret models | - select a model interpreter - generate feature importance data |
| Describe fairness considerations for models | - evaluate model fairness based on prediction disparity - mitigate model unfairness |
| Describe privacy considerations for data | - describe principles of differential privacy - specify acceptable levels of noise in data and the effects on privacy |
Microsoft DP-100: Career Prospects
After successfully passing the Microsoft DP-100 exam, you will obtain the Microsoft Certified: Azure Data Scientist Associate certification. Getting certified will allow you to qualify for several positions, including the following titles:
- Administrative Analyst
- Data Analyst
- Data Engineer
- Software Developer
Obtaining this certification is also beneficial from a financial point of view. In fact, the average salary that a certified professional can earn is $96,642 per year.
Having more competitive advantage means that you will have more opportunities and have a job that will satisfy you. This is why more and more people have long been eager for the certification of DP-100日本語. There is no doubt that obtaining this DP-100日本語 certification is recognition of their ability so that they can find a better job and gain the social status that they want. Most people are worried that it is not easy to obtain the certification of DP-100日本語, so they dare not choose to start. We are willing to appease your troubles and comfort you. We are convinced that our DP-100日本語 test material can help you solve your problems. Compared to other learning materials, our products are of higher quality and can give you access to the DP-100日本語 certification that you have always dreamed of. Now let me introduce our DP-100日本語 test questions for you. I will show you our study materials.
Skills Covered
To nail DP-100, you will need to scrutinize the below-mentioned areas:
- Execute Experiments & Train Models
This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.
- Set up Azure ML Workspace
The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.
- Deploy and Consume Models
The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.
- Manage and Optimize Models
Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Microsoft DP-100 Practice Test Questions, Microsoft DP-100 Exam Practice Test Questions
The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure exam evaluates the skills and knowledge of the professionals in a range of technical tasks. These include training models and running experiments, setting up an Azure ML workspace, consuming and deploying models, and managing & optimizing models. This test is the only qualifying exam that leads to the Microsoft Certified: Azure Data Scientist Associate certification.
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