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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.
Hvert spørsmål har en detaljert forklaring og lenker til referansemateriale for å støtte svarene som sikrer nøyaktighet av problemløsningene.
Spørsmålene vil bli blandet hver gang du gjentar testene, så du må vite hvorfor svaret er riktig, ikke bare at det riktige svaret var element “B” Microsoft AZURE DP-300 SQL Database Admin.
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
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create an Azure Machine Learning workspace
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configure workspace settings
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manage a workspace by using Azure Machine Learning studio
Manage data objects in an Azure Machine Learning workspace
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register and maintain datastores
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create and manage datasets
Manage experiment compute contexts
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create a compute instance
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determine appropriate compute specifications for a training workload
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create compute targets for experiments and training
Kjør eksperimenter og togmodeller (25-30%)
Lag modeller ved å bruke Azure Machine Learning Designer
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opprette en opplæringspipeline ved å bruke Azure Machine Learning-designer
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innta data i en designerpipeline
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bruke designermoduler for å definere en rørledningsdataflyt
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bruk tilpassede kodemoduler i designer
Kjør opplæringsskript i et Azure Machine Learning-arbeidsområde
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opprette og kjøre et eksperiment ved å bruke Azure Machine Learning SDK
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konfigurere kjøreinnstillinger for et skript
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konsumere data fra et datasett i et eksperiment ved å bruke Azure Machine Learning SDK
Generer beregninger fra en eksperimentkjøring
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loggberegninger fra en eksperimentkjøring
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hente og se eksperimentutdata
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bruke logger for å feilsøke eksperimentkjøringsfeil
Automatiser modellopplæringsprosessen
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opprette en pipeline ved å bruke SDK
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sende data mellom trinn i en pipeline
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kjøre en rørledning
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monitor pipeline runs
Optimize and Manage Models (20-25%)
Use Automated ML to create optimal models
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use the Automated ML interface in Azure Machine Learning studio
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use Automated ML from the Azure Machine Learning SDK
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select pre-processing options
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determine algorithms to be searched
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define a primary metric
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get data for an Automated ML run
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retrieve the best model
Use Hyperdrive to tune hyperparameters
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select a sampling method
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define the search space
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define the primary metric
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define early termination options
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find the model that has optimal hyperparameter values
Use model explainers to interpret models
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select a model interpreter
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generate feature importance data
Manage models
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register a trained model
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monitor model usage
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monitor data drift
Deploy and Consume Models (20-25%)
Create production compute targets
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consider security for deployed services
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evaluate compute options for deployment
Deploy a model as a service
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configure deployment settings
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consume a deployed service
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troubleshoot deployment container issues
Create a pipeline for batch inferencing
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publish a batch inferencing pipeline
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run a batch inferencing pipeline and obtain outputs
Publish a designer pipeline as a web service
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create a target compute resource
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configure an Inference pipeline
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consume a deployed endpoint
Eksamenen er tilgjengelig på følgende språk: Engelsk, Japansk, kinesisk (Forenklet), Koreansk
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