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In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.

The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.

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De Azure Data Scientist past zijn kennis van datawetenschap en machine learning toe om machine learning-workloads op Azure te implementeren en uit te voeren; vooral, using Azure Machine Learning Service and Azure Databricks. Dit omvat het plannen en creëren van een geschikte werkomgeving voor data science-workloads op Azure, het uitvoeren van gegevensexperimenten en het trainen van voorspellende modellen, het beheren en optimaliseren van modellen, 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. In aanvulling op, 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

Run Experiments and Train Models (25-30%)

Create models by using 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 training scripts in an Azure Machine Learning workspace

• 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

Generate metrics from an experiment run

• log metrics from an experiment run

• retrieve and view experiment outputs

• use logs to troubleshoot experiment run errors

Automate the model training process

• create a pipeline by using the SDK

• pass data between steps in a pipeline

• run a pipeline

• 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

Data Engineering op Microsoft Azure moet gedegen kennis hebben van talen voor gegevensverwerking: Engels, Japans, Chinese (Vereenvoudigd), Koreaans