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Microsoft Certified: Azure Data Scientist Associate - Full exam preparation

Real exam details:

Duration: 180 Minutes, No. Of Questions: 40-60, Passing Score: 70%.

This course provides overall 340 unique questions for your Azure exam preparation!

We always want to deliver highest quality to you and we made our best to do so. If you find any issue let me know and we will correct it immediatelly :)

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• 23 percent received up to a 20 percent salary increase after obtaining certification

• Nearly 65 percent of respondents received a positive impact on their professional image or reputation after obtaining certification

• 54 percent of those who obtained certifications experienced a career benefit within three months, and 24 percent experienced the benefit immediately.

Benefeits of getting further certified:

• Added credibility

• Good job opportunities

• Azure Experts are in Demand

• More than 80 percent of Fortune 500 companies are taking advantage of Microsoft Cloud

• Data shows that around 90 percent of companies are taking some advantage of cloud technology

Exam Concepts:

• 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%)

  
  

This course provide overall 540 unique questions for your exam preparation. There are no duplicated questions. All questions are multiple choice with one or several correct answer. You will get the information of how many answers are correct for each question as in the real exam.

There are 5 test exams for you. Each has 60 questions as the real exam has.

There sixth exam test contains 240 questions. You should do this test, after solving the first 5 tests with a very good score

You will learn about the following contents as they can be covered by the exam:

Exam Topics

Manage Azure resources for machine learning (25-30%)

• 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

• Manage compute for experiments in Azure Machine Learning

  • determine the appropriate compute specifications for a training workload

  • create compute targets for experiments and training

  • configure Attached Compute resources including Azure Databricks

  • monitor compute utilization

• Implement security and access control in Azure Machine Learning

  • determine access requirements and map requirements to built-in roles

  • create custom roles

  • manage role membership

  • manage credentials by using Azure Key Vault

• Set up an Azure Machine Learning development environment

  • create compute instances

  • share compute instances

  • access Azure Machine Learning workspaces from other development environments

• Set up an Azure Databricks workspace

  • create an Azure Databricks workspace

  • create an Azure Databricks cluster

  • create and run notebooks in Azure Databricks

  • link and Azure Databricks workspace to an Azure Machine Learning workspace

Run experiments and train models (20-25%)

• Create models by using the 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 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 compute

  • 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

  • track experiments running in Azure Databricks

• Use Automated Machine Learning 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

  • select the algorithms to be searched

  • define a primary metric

  • get data for an Automated ML run

  • retrieve the best model

• Tune hyperparameters with Azure Machine Learning

  • select a sampling method

  • define the search space

  • define the primary metric

  • define early termination options

  • find the model that has optimal hyperparameter values

Deploy and operationalize machine learning solutions (35-40%)

• Select compute for model deployment

  • consider security for deployed services

  • evaluate compute options for deployment

• Deploy a model as a service

  • configure deployment settings

  • 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

• Manage models in Azure Machine Learning

  • register a trained model

  • monitor model usage

  • monitor data drift

• Create an Azure Machine Learning pipeline for batch inferencing

  • configure a ParallelRunStep

  • configure compute for a batch inferencing pipeline

  • publish a batch inferencing pipeline

  • run a batch inferencing pipeline and obtain outputs

  • obtain outputs from a ParallelRunStep

• Publish an Azure Machine Learning designer pipeline as a web service

  • create a target compute resource

  • configure an inference pipeline

  • consume a deployed endpoint

• Implement pipelines by using the Azure Machine Learning SDK

  • create a pipeline

  • pass data between steps in a pipeline

  • run a pipeline

  • monitor pipeline runs

• Apply ML Ops practices

  • trigger an Azure Machine Learning pipeline from Azure DevOps

  • automate model retraining based on new data additions or data changes

  • refactor notebooks into scripts

  • implement source control for scripts

Implement responsible machine learning (5-10%)

• 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

In case you have questions, do not hesitate to contact us.

Please be aware that we are working on this course on an ongoing basis. We always want to deliver highest quality to you and we try our best to do so. If you find any issue let us know and we will correct it immediatelly :)