High pass rate
Our DP-100 study guide almost covers all of the key points and the newest question types in the IT exam, what's more, there are explanations for some answers of the difficult questions in the DP-100 exam materials that can let the buyers have a better understanding of these difficult questions, with which there is no doubt that you can pass the exam much easier. The feedbacks from our customers have shown that with the help of our DP-100 practice questions, the pass rate has reached as high as 98%~100%, which is the highest pass rate in the IT field. So if you really want to pass the IT exam and get the IT certification, do not wait any more, our DP-100 exam study guide materials are the most suitable and the most useful study materials for you.
We have been engaged in all kinds of exams since we are little children, and we have learned from so many exam experiences that how important it is to know the key points and the question types before the exam. Now, there is good news for the IT workers who are preparing for the DP-100 test. I am glad to tell you that our company has employed a lot of top IT experts who are from different countries to compile the DP-100 exam materials for IT exam during the 10 years, and we have made great achievements in this field. Now, our DP-100 practice questions have received warm reception from many countries and have become the leader in this field, the reasons are as follows.
Download the free demo before buying
Our customers are all over the world, and our DP-100 exam materials are very popular in many countries since they come out. If you still have any misgivings, just take it easy, we can fully understand you, but please click into our website and download the free demo of DP-100 study guide before you make a decision. We provide three kinds of demo versions for our customers, and welcome everyone to have a try. We believe that you will be attracted by the helpful contents in our DP-100 practice questions, and we are look forward to your success in the near future.
High safety for the information of our customers
There is no need for you to worry about the safety of your personal information, because one of the biggest advantages of buying DP-100 exam materials from our website is that we will spare no effort to guarantee the privacy of our customers. We have always attached great importance to the protection of the information of our customers, and our operation system will record the e-mail address you registered, and will send the DP-100 exam study guide to your e-mail automatically after payment, and in the process, your information is completely confidential. In addition, our company has carried out cooperation with the trustworthy payment platform. We sincerely will protect your interests in our DP-100 practice questions from any danger. You can share free shopping.
After purchase, Instant Download: Upon successful payment, Our systems will automatically send the product you have purchased to your mailbox by email. (If not received within 12 hours, please contact us. Note: don't forget to check your spam.)
Target Audience & Requirements
The candidates for this Microsoft exam are Azure Data Scientists. These professionals have expertise in applying their knowledge of machine learning and data science to run and implement ML workloads on Azure. This is particularly in the usage of Azure ML Service. These applicants are the experts in planning and creating the appropriate working environments for data science workloads within Azure. They also train predictive models and run data experiments. The individuals who want to earn ACE college credit can also take this certification test.
The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure test has no official requirement. However, the candidates must develop an in-depth understanding of the exam topics. They should also have expertise in model optimization and management and ML models deployment within the production.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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 |
1351 Customer Reviews
