Microsoft Certified: Azure Data Scientist Associate

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. In addition, 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

Manage Azure resources for machine learning
Run experiments and train models
Deploy and operationalize machine learning solutions
Implement responsible machine learning

Course Outline

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

Create models by using the Azure Machine Learning designer

create a training pipeline by using Azure Machine Learning designer
ingest data in a designer pipelin
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 computer
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

Select compute for model deployment

consider security for deployed services
evaluate compute options for deployment

Deploy a model as a service

configure deployment setting
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

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

About The Exam

This exam measures your ability to accomplish the following technical tasks: manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions; and implement responsible machine learning.

International student Fee 750$

Microsoft Certification (Free Practice Exam Dumps)