Azure Data Scientist Associate - United Kingdom - Robust IT Training Ltd

Description

About the
course

Exam Prefix DP-100

Level Associate

Length 60 minutes to complete the exam

Candidates for the Azure Data Scientist Associate certification should havesubject matter expertise applying data science and machine learning toimplement and run machine learning workloads on Azure.

Responsibilities for this role include planning and creating a suitableworking environment for data science workloads on Azure. You run dataexperiments and train predictive models. In addition, you manage, optimize,and deploy machine learning models into production.

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

Manage Azure resources for machine learning

Run experiments and train models

Deploy and operationalize machine learning solutions

Average salary for Azure Data Scientistprofessionals

£ 60, The average azure data scientist salary in the United Kingdom is £60,000 per year or£30.77 per hour. Entry level positions start at £52,500 per year while most experiencedworkers make up to £68,875 per year .

Candidates for this exam should have subject matter expertiseapplying data science and machine learning to implement and runmachine learning workloads on Microsoft Azure.

Responsibilities for this role include planning and creating asuitable working environment for data science workloads on Azure.They run data experiments and train predictive models. In addition,they manage, optimize, and deploy machine learning models intoproduction.

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

• The content of this exam was updated on May 20, 2021. Pleasedownload the exam skills outline below to see what changed
• Manage Azure resources for machine learning (25–30%)
• Run experiments and train models (20–25%)
• Deploy and operationalize machine learning solutions (35–40%)
• Implement responsible machine learning (5–10%)

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