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Microsoft Exam DP-100 Questions Answers Test Simulator

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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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