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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

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Microsoft DP-100: Exam Details

Microsoft doesn’t provide detailed information regarding its exams, including the DP-100 certification test. The total number of questions in exam is not published on the official website, but the candidates can expect to encounter about 40-60 items in their delivery of their test. There is also a high probability that they will face scenario-based questions with single-answer and multiple-answer options. The duration is all about 120 minutes. The exam is available in several languages, including Japanese, English, Simplified Chinese, and Korean.

The Microsoft DP-100 exam can be taken at any nearest Pearson VUE testing center or online as a proctored option. The cost for the exam is $165 in the United States but the price is usually different in other countries due to taxation. To find out the actual pricing for your country, simply check the official webpage.

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

DP-100 Exam topics

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our DP-100 exam dumps will include the following topics: