Microsoft Designing and Implementing a Data Science Solution on Azure Exam Syllabus

Designing and Implementing a Data Science Solution on Azure PDF, DP-100 Dumps, DP-100 PDF, Designing and Implementing a Data Science Solution on Azure VCE, DP-100 Questions PDF, Microsoft DP-100 VCE, Microsoft Designing and Implementing a Data Science Solution on Azure Dumps, Microsoft Designing and Implementing a Data Science Solution on Azure PDFUse this quick start guide to collect all the information about Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the DP-100 Designing and Implementing a Data Science Solution on Microsoft Azure exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft Designing and Implementing a Data Science Solution on Azure certification exam.

The Microsoft Designing and Implementing a Data Science Solution on Azure certification is mainly targeted to those candidates who want to build their career in Microsoft Azure domain. The Microsoft Certified - Azure Data Scientist Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft Designing and Implementing a Data Science Solution on Azure.

Microsoft Designing and Implementing a Data Science Solution on Azure Exam Summary:

Exam Name Microsoft Certified - Azure Data Scientist Associate
Exam Code DP-100
Exam Price $165 (USD)
Duration 120 mins
Number of Questions 40-60
Passing Score 700 / 1000
Books / Training DP-100T01-A: Designing and Implementing a Data Science Solution on Azure
Schedule Exam Pearson VUE
Sample Questions Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions
Practice Exam Microsoft DP-100 Certification Practice Exam

Microsoft DP-100 Exam Syllabus Topics:

Topic Details

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 ML (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

To ensure success in Microsoft Designing and Implementing a Data Science Solution on Azure certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Designing and Implementing a Data Science Solution on Microsoft Azure (DP-100) exam.

Rating: 5 / 5 (53 votes)