Øv eksamener | MS Azure DP-100 Design & Implement DS Sol

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Microsoft AZURE DP-300 SQL Database Admin, Microsoft AZURE DP-300 SQL Database Admin: Microsoft AZURE DP-300 SQL Database Admin. Microsoft AZURE DP-300 SQL Database Admin. Microsoft AZURE DP-300 SQL Database Admin.

Microsoft AZURE DP-300 SQL Database Admin. Microsoft AZURE DP-300 SQL Database Admin.

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The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; spesielt, using Azure Machine Learning Service and Azure Databricks. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.

Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. I tillegg, you manage, optimize, and deploy machine learning models into production.

A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.

Skills measured on Microsoft Azure DP-100 Exam

Set up an Azure Machine Learning Workspace (30-35%)

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 objects in an Azure Machine Learning workspace

• register and maintain datastores

• create and manage datasets

Manage experiment compute contexts

• create a compute instance

• determine appropriate compute specifications for a training workload

• create compute targets for experiments and training

Kjør eksperimenter og togmodeller (25-30%)

Lag modeller ved å bruke Azure Machine Learning Designer

• opprette en opplæringspipeline ved å bruke Azure Machine Learning-designer

• innta data i en designerpipeline

• bruke designermoduler for å definere en rørledningsdataflyt

• bruk tilpassede kodemoduler i designer

Kjør opplæringsskript i et Azure Machine Learning-arbeidsområde

• opprette og kjøre et eksperiment ved å bruke Azure Machine Learning SDK

• konfigurere kjøreinnstillinger for et skript

• konsumere data fra et datasett i et eksperiment ved å bruke Azure Machine Learning SDK

Generer beregninger fra en eksperimentkjøring

• loggberegninger fra en eksperimentkjøring

• hente og se eksperimentutdata

• bruke logger for å feilsøke eksperimentkjøringsfeil

Automatiser modellopplæringsprosessen

• opprette en pipeline ved å bruke SDK

• sende data mellom trinn i en pipeline

• kjøre en rørledning

• monitor pipeline runs

Optimize and Manage Models (20-25%)

Use Automated ML 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

• determine algorithms to be searched

• define a primary metric

• get data for an Automated ML run

• retrieve the best model

Use Hyperdrive to tune hyperparameters

• select a sampling method

• define the search space

• define the primary metric

• define early termination options

• find the model that has optimal hyperparameter values

Use model explainers to interpret models

• select a model interpreter

• generate feature importance data

Manage models

• register a trained model

• monitor model usage

• monitor data drift

Deploy and Consume Models (20-25%)

Create production compute targets

• consider security for deployed services

• evaluate compute options for deployment

Deploy a model as a service

• configure deployment settings

• consume a deployed service

• troubleshoot deployment container issues

Create a pipeline for batch inferencing

• publish a batch inferencing pipeline

• run a batch inferencing pipeline and obtain outputs

Publish a designer pipeline as a web service

• create a target compute resource

• configure an Inference pipeline

• consume a deployed endpoint

Eksamenen er tilgjengelig på følgende språk: Engelsk, Japansk, kinesisk (Forenklet), Koreansk