Manage Azure subscriptions

• assign administrator permissions
• configure cost center quotas and tagging
• configure policies at Azure subscription level
• implement Management Groups

Analyze resource utilization and consumption

• configure diagnostic settings on resources
• create baseline for resources
• create and test alerts
• analyze alerts across subscription
• analyze metrics across subscription
• create action groups and action rules
• monitor for unused resources
• monitor spends
• report on spend
• utilize log queries in Azure Monitor
• view alerts in Azure Monitor

Manage resource groups

• use Azure policies for resource groups
• configure resource locks
• configure resource policies
• implement and set tagging on resource groups
• move resources across resource groups
• remove resource groups

Managed role-based access control (RBAC)

• create a custom role
• configure access to Azure resources by assigning roles
• configure management access to Azure
• troubleshoot RBAC
• implement RBAC policies
• assign RBAC Roles

Create and configure storage accounts

• configure network access to the storage account
• create and configure storage account
• generate shared access signature
• install and use Azure Storage Explorer
• manage access keys
• monitor activity log by using Monitor Logs
• implement Azure storage replication
• implement Azure AD Authentication

Import and export data to Azure

• create export from Azure job
• create import into Azure job
• use Azure Data Box
• configure and use Azure blob storage
• configure Azure content delivery network (CDN) endpoints
• use Azure Data Factory to transfer data to Azure

Configure Azure files

• create Azure file share
• create Azure File Sync service
• create Azure sync group
• troubleshoot Azure File Sync

Implement Azure backup

• configure and review backup reports
• perform backup operation
• create Recovery Services Vault
• create and configure backup policy
• perform a restore operation

Create and configure a VM for Windows and Linux

• configure high availability
• configure monitoring, networking, storage, and virtual machine size
• deploy and configure scale sets

Automate deployment of VMs

• modify Azure Resource Manager (ARM) template
• configure location of new VMs
• configure VHD template
• deploy from template
• save a deployment as an ARM template
• deploy Windows and Linux VMs

Manage Azure VM

• add data discs
• add network interfaces
• automate configuration management by using PowerShell Desired State Configuration

(DSC) and VM Agent by using custom script extensions

• manage VM sizes
• move VMs from one resource group to another
• redeploy VMs
• soft delete for Azure VMs

Manage VM backups

• configure VM backup
• define backup policies
• implement backup policies
• perform VM restore
• Azure Site Recovery

Create connectivity between virtual networks

• create and configure VNET peering
• create and configure VNET to VNET connections
• verify virtual network connectivity
• create virtual network gateway

Implement and manage virtual networking

• configure private and public IP addresses, network routes, network interface, subnets, and virtual network

Configure name resolution

• configure Azure DNS
• configure custom DNS settings
• configure private and public DNS zones

Create and configure a Network Security Group (NSG)

• create security rules
• associate NSG to a subnet or network interface
• identify required ports
• evaluate effective security rules
• implement Application Security Groups

Implement Azure load balancer

• configure internal load balancer
• configure load balancing rules
• configure public load balancer
• troubleshoot load balancing

Monitor and troubleshoot virtual networking

• monitor on-premises connectivity
• use Network resource monitoring
• use Network Watcher
• troubleshoot external networking
• troubleshoot virtual network connectivity

Integrate on premises network with Azure virtual network

• create and configure Azure VPN Gateway
• create and configure site to site VPN
• configure Express Route
• verify on premises connectivity
• troubleshoot on premises connectivity with Azure
• use Azure network adapter

Manage Azure Active Directory (AD)

• add custom domains
• Azure AD Join
• configure self-service password reset
• manage multiple directories

Manage Azure AD objects (users, groups, and devices)

• create users and groups
• manage user and group properties
• manage device settings
• perform bulk user updates
• manage guest accounts

Implement and manage hybrid identities

• install Azure AD Connect, including password hash and pass-through synchronization use Azure AD Connect to configure federation with on-premises Active Directory Domain
• Services (AD DS)
• manage Azure AD Connect
• manage password sync and password writeback

 Implement multi-factor authentication (MFA)

• configure user accounts for MFA
• enable MFA by using bulk update
• configure fraud alerts
• configure bypass options
• configure Trusted IPs
• configure verification methods

Identify features of common AI workloads

• identify prediction/forecasting workloads
• identify features of anomaly detection workloads
• identify computer vision workloads
• identify natural language processing or knowledge mining workloads
• identify conversational AI workloads

Identify guiding principles for responsible AI

• describe considerations for fairness in an AI solution
• describe considerations for reliability and safety in an AI solution
• describe considerations for privacy and security in an AI solution
• describe considerations for inclusiveness in an AI solution
• describe considerations for transparency in an AI solution
• describe considerations for accountability in an AI solution

Identify common machine learning types

• identify regression machine learning scenarios
• identify classification machine learning scenarios
• identify clustering machine learning scenarios

Describe core machine learning concepts

• identify features and labels in a dataset for machine learning
• describe how training and validation datasets are used in machine learning
• describe how machine learning algorithms are used for model training
• select and interpret model evaluation metrics for classification and regression

Identify core tasks in creating a machine learning solution

• describe common features of data ingestion and preparation
• describe common features of feature selection and engineering
• describe common features of model training and evaluation
• describe common features of model deployment and management

Describe capabilities of no-code machine learning with Azure Machine Learning

• automated Machine Learning UI
• azure Machine Learning designer

Identify common types of computer vision solution

• identify features of image classification solutions
• identify features of object detection solutions
• identify features of semantic segmentation solutions
• identify features of optical character recognition solutions
• identify features of facial detection, facial recognition, and facial analysis solutions

Identify Azure tools and services for computer vision tasks

• identify capabilities of the Computer Vision service
• identify capabilities of the Custom Vision service
• identify capabilities of the Face service
• identify capabilities of the Form Recognizer service

Identify features of common NLP Workload Scenarios

• identify features and uses for key phrase extraction
• identify features and uses for entity recognition
• identify features and uses for sentiment analysis
• identify features and uses for language modeling
• identify features and uses for speech recognition and synthesis
• identify features and uses for translation

Identify Azure tools and services for NLP workloads

• identify capabilities of the Text Analytics service
• identify capabilities of the Language Understanding Intelligence Service (LUIS)
• identify capabilities of the Speech service
• identify capabilities of the Translator Text service

Identify common use cases for conversational AI

• identify features and uses for webchat bots
• identify features and uses for telephone voice menus
• identify features and uses for personal digital assistants
• identify common characteristics of conversational AI solutions

Identify Azure services for conversational AI

• identify capabilities of the QnA Maker service
• identify capabilities of the Bot Framework

Identify features of common AI workloads

• identify prediction/forecasting workloads
• identify features of anomaly detection workloads
• identify computer vision workloads
• identify natural language processing or knowledge mining workloads
• identify conversational AI workloads

Identify guiding principles for responsible AI

• describe considerations for fairness in an AI solution
• describe considerations for reliability and safety in an AI solution
• describe considerations for privacy and security in an AI solution
• describe considerations for inclusiveness in an AI solution
• describe considerations for transparency in an AI solution
• describe considerations for accountability in an AI solution

Identify common machine learning types

• identify regression machine learning scenarios
• identify classification machine learning scenarios
• identify clustering machine learning scenarios

Describe core machine learning concepts

• identify features and labels in a dataset for machine learning
• describe how training and validation datasets are used in machine learning
• describe how machine learning algorithms are used for model training
• select and interpret model evaluation metrics for classification and regression

Identify core tasks in creating a machine learning solution

• describe common features of data ingestion and preparation
• describe feature engineering and selection
• describe common features of model training and evaluation
• describe common features of model deployment and management

Describe capabilities of no-code machine learning with Azure Machine Learning studio

• automated ML Wizard UI
• azure Machine Learning designer

Identify common types of computer vision solution

• identify features of image classification solutions
• identify features of object detection solutions
• identify features of semantic segmentation solutions
• identify features of optical character recognition solutions
• identify features of facial detection, facial recognition, and facial analysis solutions

Identify Azure tools and services for computer vision tasks

• identify capabilities of the Computer Vision service
• identify capabilities of the Custom Vision service
• identify capabilities of the Face service
• identify capabilities of the Form Recognizer service

Identify features of common NLP Workload Scenarios

• identify features and uses for key phrase extraction
• identify features and uses for entity recognition
• identify features and uses for sentiment analysis
• identify features and uses for language modeling
• identify features and uses for speech recognition and synthesis
• identify features and uses for translation

Identify Azure tools and services for NLP workloads

• identify capabilities of the Text Analytics service
• identify capabilities of the Language Understanding service (LUIS)
• identify capabilities of the Speech service

Identify common use cases for conversational AI

• identify features and uses for webchat bots
• identify features and uses for telephone voice menus
• identify features and uses for personal digital assistants
• identify common characteristics of conversational AI solutions

Identify Azure services for conversational AI

• identify capabilities of the QnA Maker service
• identify capabilities of the Azure Bot service

• select the processing architecture for a solution
• select the appropriate data processing technologies
• select the appropriate AI models and services
• identify components and technologies required to connect service endpoints
• identify automation requirements

• identify processes and regulations needed to conform with data privacy, protection, and
• regulatory requirements
• identify which users and groups have access to information and interfaces
• identify appropriate tools for a solution
• identify auditing requirements

• identify appropriate services and tools for a solution
• identify integration points with other Microsoft services
• identify storage required to store logging, bot state data, and Azure Cognitive Services output

Design solutions that include one or more pipelines

• define an AI application workflow process
• design a strategy for ingest and egress data
• design the integration point between multiple workflows and pipelines
• design pipelines that use AI apps
• design pipelines that call Azure Machine Learning models
• select an AI solution that meet cost constraints

• design solutions that use vision, speech, language, knowledge, search, and anomaly
• detection APIs

• integrate bots and AI solutions
• design bot services that use Language Understanding (LUIS)
• design bots that integrate with channels
• integrate bots with Azure app services and Azure Application Insights

• identify whether to create a GPU, FPGA, or CPU-based solution
• identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
• select a compute solution that meets cost constraints

• define how users and applications will authenticate to AI services
• design a content moderation strategy for data usage within an AI solution
• ensure that data adheres to compliance requirements defined by your organization
• ensure appropriate governance of data
• design strategies to ensure that the solution meets data privacy regulations and industry standards

• Implement an AI workflow

  • develop AI pipelines
  • manage the flow of data through the solution components
  • implement data logging processes
  • define and construct interfaces for custom AI services
  • create solution endpoints
  • develop streaming solutions

• configure prerequisite components and input datasets to allow the consumption of Azure Cognitive Services APIs
• configure integration with Azure Cognitive Services
• configure prerequisite components to allow connectivity to the Microsoft Bot Framework
• implement Azure Cognitive Search in a solution

• identify the differences between KPIs, reported metrics, and root causes of thedifferences
• identify the differences between expected and actual workflow throughput
• maintain an AI solution for continuous improvement
• monitor AI components for availability
• recommend changes to an AI solution based on performance data

Recommend Azure Cognitive Services APIs to meet business requirements

• select the processing architecture for a solution
• select the appropriate data processing technologies
• select the appropriate AI models and services
• identify components and technologies required to connect service endpoints
• identify automation requirements

• identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
• identify which users and groups have access to information and interfaces
• identify appropriate tools for a solution
• identify auditing requirements

• identify appropriate services and tools for a solution
• identify integration points with other Microsoft services
• identify storage required to store logging, bot state data, and Azure Cognitive Services output

• Design solutions that include one or more pipelines

  • define an AI application workflow process
  • design a strategy for ingest and egress data
  • design the integration point between multiple workflows and pipelines
  • design pipelines that use AI apps
  • design pipelines that call Azure Machine Learning models
  • select an AI solution that meet cost constraints

• design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs

• integrate bots and AI solutions
• design bot services that use Language Understanding (LUIS)
• design bots that integrate with channels
• integrate bots with Azure app services and Azure Application Insights

• identify whether to create a GPU, FPGA, or CPU-based solution
• identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
• select a compute solution that meets cost constraints

• define how users and applications will authenticate to AI services
• design a content moderation strategy for data usage within an AI solution
• ensure that data adheres to compliance requirements defined by your organization
• ensure appropriate governance of data
• design strategies to ensure that the solution meets data privacy regulations and industry standards

• Implement an AI workflow

  • develop AI pipelines
  • manage the flow of data through the solution components
  • implement data logging processes
  • define and construct interfaces for custom AI services
  • create solution endpoints
  • develop streaming solutions

• configure prerequisite components and input datasets to allow the consumption of
• Azure Cognitive Services APIs
• configure integration with Azure Cognitive Services
• configure prerequisite components to allow connectivity to the Microsoft Bot Framework
• implement Azure Search in a solution

• identify the differences between KPIs, reported metrics, and root causes of the differences
• identify the differences between expected and actual workflow throughput
• maintain an AI solution for continuous improvement
• monitor AI components for availability
• recommend changes to an AI solution based on performance data

Description
Goal & Objectives
Agenda

Overview and Objectives
Third-Party Payroll Connectors
Payroll-Related Events

Overview and Objectives
Integration System Setup
Create Payroll Interface and Assign Integration System User
Generate Full File
Integration System Configuration
Enable Service and Field Attributes
Include Additional Fields and Map Values
Integration Launch
Compare Pay Groups
Close Out Pay Period and Use Earliest Open Pay Period.

Overview and Objectives
Setup Checklist
Payroll Framework
Tenant Setup

Overview and Objectives
Dated Data and Comparison Dates
Change Detection Method and Parameters
Enable Change Detection for Payroll Interface
Change Detection Rules
Hire Four New Workers and Test Change Detection
Use the Earliest Open Pay Period to Pick Up Future Dated Events
Primary Integrations and Change Detection
Transaction Log
Set Transaction Log
ReSet Transaction Log – Termination
Change Detection Summary

Overview and Objectives
Employee/Payee Object Overview
Personal Data
Personal and Position Data Setup
Personal Data – Name
Personal Data: Name Components and Regional Differences
Personal Data: Address Formats and Regional Differences
Identifiers: Personal Data
Position Data
Organizations
Position Data: Supervisory Organization
Time Tracking and Time Off
Time Off
Payroll Interface Audit File Processes
Payee Data Validation and Error Handling

Overview and Objectives
Payroll Effective Change Interface Setup
Create a PECI Integration System
Launch Parameters
Full File for PECI
Configure Field Attributes and Launch Ad-Hoc Run
Change Worker Data
XML Output for PECI
Additional Worker Data Changes
Effective Stack
Set Last Extract Created Date and Schedule Primary Integration
PECI Summary

Overview and Objectives
PECI Personal Data
PECI Name and Address Change
Personal Data Output
PECI Personal Data: Identifiers
PECI Position Data
PECI Position Data: Organizations
PECI Audit Files and Troubleshooting
Explore PECI Audit Files

Overview and Objectives
Configuration Comparison
Staffing Events
Job Change and Pay Company Transfer
Leave of Absence
Termination Event
Payment Elections
Compensation Change
Earnings and Deductions
One-Time Payment and Use Cases and Limitations

Overview and Objectives
Business Processes and Integrations
Document Delivery
Transformation Options
Document Transformation for PI
Create Document Transformation System for PI
“Stitch” PI System to Document Transformation
Document Transformation for PECI
Set Up DT for PECI Green Planet Pay Group
Set Up Document Transformation for PECI UK Pay group
Multiple Output Files for PI (HomeWork)
XML Basics
Document Transform Processing (HOMEWORK)
Document Transformation and ETV/XTT
XTT Separator
Transforming PECI
Non-Effective Dated Change
PECI Prior Value
Utilizing Operations and Change Flags in XSLT
Position Transfer
Multiple Positions
DT Best Practices.

Overview of Workday Extend Platform
Use Cases & Benefits
Structure of Extend Applications
Introduction to PMD, AMD, and SMD

PMD: Page configuration, layout controls, file structure
AMD: App-level settings and deployment
SMD: Service layer configuration
Demo: Walkthrough of a PMD file and rendering UI

Creating and using Endpoints
Types of Authentication: OAuth, Basic Auth
Secure endpoint configuration
Demo: Secure endpoint call from Extend App

Common Widgets: TextInput, Dropdown, Button
Layout and conditional logic
Demo: Dynamic widget rendering

Exception handling in services and UI
Setting up and using Flow Variables
Best practices in state management
Demo: Pass state between pages using flow variables

Using query parameters in Extend
Outbound variables for data flow
Demo: Transfer user inputs between screens

Creating and using PMD functions
Parameterization and logic
Demo: Conditional rendering using PMD functions

Security Domain: Defining access
Business Objects: Designing and using
Task & Attachments management
Integration with Workday BPs
Demo: Creating and securing a custom BO

Types of Orchestrations
Calling orchestrations from PMD
Data mapping and response handling
Demo: Orchestration call from UI

Full App Review: PMD, Orchestration, BO
Live demo and discussion
Deployment tips & best practices
Open Q&A