Microsoft Exam DP-100 Questions Answers Test Simulator

Dp-100 Exam Topics

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 machine learning 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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