Course Overview

Design and build secure, reliable, and scalable AWS-based applications.

In this course, you learn how to use the AWS SDK to develop secure and scalable cloud applications using multiple AWS services such as Amazon DynamoDB, Amazon Simple Storage Service, and AWS Lambda. You explore how to interact with AWS using code and learn about key concepts, best practices, and troubleshooting tips.

The final day of this course is the Developing on AWS Jam, a gamified activity with teams competing to win by completing a series of hands-on challenges on the AWS platform over the course of the day. Participants will have the opportunity to develop, enhance, and validate skillsets in the AWS Cloud through real-world problem solving

Course Objectives

  • Set up the AWS SDK and developer credentials for Java, C#/.NET, Python, and JavaScript
  • Interact with AWS services and develop solutions by using the AWS SDK
  • Use AWS Identity and Access Management (IAM) for service authentication
  • Use Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB as data stores
  • Integrate applications and data by using AWS Lambda, Amazon API Gateway, Amazon Simple Queue Service (Amazon SQS), Amazon Simple Notification Service (Amazon SNS), and AWS Step Functions
  • Use Amazon Cognito for user authentication
  • Use Amazon ElastiCache to improve application scalability
  • Leverage the CI/CD pipeline to deploy applications on AWS
  • Final Day of Developing on AWS Jam
    • Hands-on format, putting learners in the driver’s seat to make decisions and solve problems in an active AWS Cloud environment to amplify understanding
    • Develop your  skillsets in the AWS Cloud to advance your cloud adoption journey
    • Reinforce your learning while it’s still fresh in your head

Course Content

Day 1

Module 0: Course Overview

  • Agenda
  • Introductions
  • Student resources

Module 1: Introduction to AWS

  • Introduction to the AWS Cloud
  • Cloud scenarios
  • Infrastructure overview
  • Introduction to AWS foundation services

Module 2: Introduction to Developing on AWS

  • Getting started with developing on AWS
  • Introduction to developer tools
  • Introduction to management tools

Module 3: Introduction to AWS Identity and Access Management

  • Shared responsibility model
  • Introduction to IAM
  • Use authentication and authorization

Module 4: Introduction to the Lab Environment

  • Introduction to the lab environment
  • Lab 1: Getting started and working with IAM

Module 5: Developing Storage Solutions with Amazon Simple Storage Service

  • Overview of AWS storage options
  • Amazon S3 key concepts
  • Best practices
  • Troubleshooting
  • Scenario: Building a complete application
  • Lab 2: Developing storage solutions with Amazon S3

Day 2

Module 6: Developing Flexible NoSQL Solutions with Amazon DynamoDB

  • Introduction to AWS database options
  • Introduction to Amazon DynamoDB
  • Developing with DynamoDB
  • Best practices
  • Troubleshooting
  • Scenario: Building an end-to-end app
  • Lab 3: Developing flexible NoSQL solutions with Amazon DynamoDB

Module 7: Developing Event-Driven Solutions with AWS Lambda

  • What is serverless computing?
  • Introduction to AWS Lambda
  • Key concepts
  • How Lambda works
  • Use cases
  • Best practices
  • Scenario: Build an end-to-end app

Module 8: Developing Solutions with Amazon API Gateway

  • Introduction to Amazon API Gateway
  • Developing with API Gateway
  • Best practices
  • Introduction to AWS Serverless Application Model
  • Scenario: Building an end-to-end app
  • Lab 4: Developing event-driven solutions with AWS Lambda

Module 9: Developing Solutions with AWS Step Functions

  • Understanding the need for Step Functions
  • Introduction to AWS Step Functions
  • Use cases

Day 3

Module 10: Developing Solutions with Amazon Simple Queue Service and Amazon Simple Notification Service

  • Why use a queueing service?
  • Developing with Amazon Simple Queue Service
  • Developing with Amazon Simple Notification Service
  • Developing with Amazon MQ
  • Lab 5: Developing messaging solutions with Amazon SQS and Amazon SNS

Module 11: Caching Information with Amazon ElastiCache

  • Caching overview
  • Caching with Amazon ElastiCache
  • Caching strategies

Module 12: Developing Secure Applications

  • Securing your applications
  • Authenticating your applications to AWS
  • Authenticating your customers
  • Scenario: Building an end-to-end app

Module 13: Deploying Applications

  • Introduction to DevOps
  • Introduction to deployment and testing strategies
  • Deploying applications with AWS Elastic Beanstalk
  • Scenario: Building an end-to-end app
  • Lab 6: Building an end-to-end app

Module 14: Course wrap-up

  • Course overview
  • AWS training courses
  • Certifications

Day 4

  • Developing on AWS Jam
  • Full day of challenges based on real-world scenarios so you can build practical skills
  • Facilitated by an expert AWS instructor who can answer questions and give real time feedback
  • Validate learning through reports on performance including benchmarks, completion times, and which challenges were the most difficult

Course Overview

This course teaches experienced developers how to programmatically interact with AWS services to build web solutions. It guides you through a high-level architectural discussion on resource selection and dives deep into using the AWS Software Development Kits (AWS SDKs) and Command Line Interface (AWS CLI)to build and deploy your cloud applications. You will build a sample application during this course, learninghow to set up permissions to the development environment, adding business logic to process data using AWS core services, configure user authentications, deploy to AWS cloud, and debug to resolve application issues. The course includes code examples to help you implement the design patterns and solutions discussed in the course. The labs reinforce key course content and help you to implement solutions using the AWS SDK for Python, .Net, and Java, the AWS CLI, and the AWS Management Console.

Course level: Intermediate

Duration: 3 days

Activities

This course includes presentations, demonstrations, and hands-on labs.

Course Objectives

In this course, you will learn to:

  • Build a simple end-to-end cloud application using AWS Software Development Kits (AWS SDKs), Command Line Interface (AWS CLI), and IDEs.
  • Configure AWS Identity and Access Management (IAM) permissions to support a development environment.
  • Use multiple programming patterns in your applications to access AWS services.
  • Use AWS SDKs to perform CRUD (create, read, update, delete) operations on Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB resources.
  • Build AWS Lambda functions with other service integrations for your web applications.
  • Understand the benefits of microservices architectures and serverless applications to design.
  • Develop API Gateway components and integrate with other AWS services.
  • Explain how Amazon Cognito controls user access to AWS resources.
  • Build a web application using Cognito to provide and control user access.
  • Use DevOps methodology to reduce the risks associated with traditional application releases and identify AWS services that help in implementing DevOps practices.
  • Use AWS Serverless Application Model (AWS SAM) to deploy an application.

Observe your application build using Amazon X-Ray

Course Content

Module 1: Course Overview

  • Logistics
  • Student resources
  • Agenda
  • Introductions

Module 2: Building a Web Application on AWS

  • Discuss the architecture of the application you are going to build during this course
  • Explore the AWS services needed to build your web application
  • Discover how to store, manage, and host your web application

Module 3: Getting Started with Development on AWS

  • Describe how to access AWS services programmatically
  • List some programmatic patterns and how they provide efficiencies within AWS SDKs and AWS CLI
  • Explain the value of AWS Cloud9

Module 4: Getting Started with Permissions

  • Review AWS Identity and Access Management (IAM) features and components permissions to support a development environment
  • Demonstrate how to test AWS IAM permissions
  • Configure your IDEs and SDKs to support a development environment
  • Demonstrate accessing AWS services using SDKs and AWS Cloud9

Lab 1: Configure the Developer Environment

  • Connect to a developer environment
  • Verify that the IDE and the AWS CLI are installed and configured to use the application profile
  • Verify that the necessary permissions have been granted to run AWS CLI commands
  • Assign an AWS IAM policy to a role to delete an Amazon S3 bucket

Module 5: Getting Started with Storage

  • Describe the basic concepts of Amazon S3
  • List the options for securing data using Amazon S3
  • Define SDK dependencies for your code
  • Explain how to connect to the Amazon S3 service
  • Describe request and response objects

Module 6: Processing Your Storage Operations

  • Perform key bucket and object operations
  • Explain how to handle multiple and large objects
  • Create and configure an Amazon S3 bucket to host a static website
  • Grant temporary access to your objects
  • Demonstrate performing Amazon S3 operations using SDKs

Lab 2: Develop Solutions Using Amazon S3

  • Interact with Amazon S3 programmatically using AWS SDKs and the AWS CLI
  • Create a bucket using waiters and verify service exceptions codes
  • Build the needed requests to upload an Amazon S3 object with metadata attached
  • Build requests to download an object from the bucket, process data, and upload the object back to the bucket
  • Configure a bucket to host the website and sync the source files using the AWS CLI
  • Add IAM bucket policies to access the S3 website.

Day 2

Module 7: Getting Started with Databases

  • Describe the key components of DynamoDB
  • Explain how to connect to DynamoDB
  • Describe how to build a request object
  • Explain how to read a response object
  • List the most common troubleshooting exceptions

Module 8: Processing Your Database Operations

  • Develop programs to interact with DynamoDB using AWS SDKs
  • Perform CRUD operations to access tables, indexes, and data
  • Describe developer best practices when accessing DynamoDB
  • Review caching options for DynamoDB to improve performance
  • Perform DynamoDB operations using SDK

Lab 3: Develop Solutions Using Amazon DynamoDB

  • Interact with Amazon DynamoDB programmatically using low-level, document, and high[1]level APIs in your programs
  • Retrieve items from a table using key attributes, filters, expressions, and paginations
  • Load a table by reading JSON objects from a file
  • Search items from a table based on key attributes, filters, expressions, and paginations
  • Update items by adding new attributes and changing data conditionally
  • Access DynamoDB data using PartiQL and object-persistence models where applicable

Module 9: Processing Your Application Logic

  • Develop a Lambda function using SDKs
  • Configure triggers and permissions for Lambda functions
  • Test, deploy, and monitor Lambda functions

Lab 4: Develop Solutions Using AWS Lambda Functions

  • Create AWS Lambda functions and interact programmatically using AWS SDKs and AWS CLI
  • Configure AWS Lambda functions to use the environment variables and to integrate with other services
  • Generate Amazon S3 pre-signed URLs using AWS SDKs and verify the access to bucket objects
  • Deploy the AWS Lambda functions with .zip file archives through your IDE and test as needed
  • Invoke AWS Lambda functions using the AWS Console and AWS CLI

Module 10: Managing the APIs

  • Describe the key components of API Gateway
  • Develop API Gateway resources to integrate with AWS services
  • Configure API request and response calls for your application endpoints
  • Test API resources and deploy your application API endpoint
  • Demonstrate creating API Gateway resources to interact with your application APIs

Lab 5: Develop Solutions Using Amazon API Gateway

  • Create RESTful API Gateway resources and configure CORS for your application
  • Integrate API methods with AWS Lambda functions to process application data
  • Configure mapping templates to transform the pass-through data during method integration
  • Create a request model for API methods to ensure that the pass-through data format complies with application rules
  • Deploy the API Gateway to a stage and validate the results using the API endpoint

Day 3

Module 11: Building a Modern Application

  • Describe the challenges with traditional architectures
  • Describe the microservice architecture and benefits
  • Explain various approaches for designing microservice applications
  • Explain steps involved in decoupling monolithic applications
  • Demonstrate the orchestration of Lambda Functions using AWS Step Functions

Module 12: Granting Access to Your Application Users

  • Analyze the evolution of security protocols
  • Explore the authentication process using Amazon Cognito
  • Manage user access and authorize serverless APIs
  • Observe best practices for implementing Amazon Cognito
  • Demonstrate the integration of Amazon Cognito and review JWT tokens

Lab 6: Capstone – Complete the Application Build

  • Create a Userpool and an Application Client for your web application using
  • Add new users and confirm their ability to sign-in using the Amazon Cognito CLI
  • Configure API Gateway methods to use Amazon Cognito as an authorizer
  • Verify JWT authentication tokens are generated during API Gateway calls
  • Develop API Gateway resources rapidly using a Swagger importing strategy
  • Set up your web application frontend to use Amazon Cognito and API Gateway configurations and verify the entire application functionality

Module 13: Deploying Your Application

  • Identify risks associated with traditional software development practices
  • Understand DevOps methodology
  • Configure an AWS SAM template to deploy a serverless application
  • Describe various application deployment strategies
  • Demonstrate deploying a serverless application using AWS SAM

Module 14: Observing Your Application

  • Differentiate between monitoring and observability
  • Evaluate why observability is necessary in modern development and key components
  • Understand CloudWatch’s part in configuring the observability
  • Demonstrate using CloudWatch Application Insights to monitor applications
  • Demonstrate using X-Ray to debug your applications

Lab 7: Observe the Application Using AWS X-Ray

  • Instrument your application code to use AWS X-Ray capabilities
  • Enable your application deployment package to generate logs
  • Understand the key components of an AWS SAM template and deploy your application
  • Create AWS X-Ray service maps to observe end-to-end processing behavior of your application
  • Analyze and debug application issues using AWS X-Ray traces and annotations

Module 15: Course Wrap-up

  • Course overview
  • AWS training courses
  • Certifications
  • Course feedback

Course Overview

In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Course Objectives

This course teaches participants the following skills:

  • Use best practices for application development.
  • Choose the appropriate data storage option for application data.
  • Implement federated identity management.
  • Develop loosely coupled application components or microservices.
  • Integrate application components and data sources.
  • Debug, trace, and monitor applications.
  • Perform repeatable deployments with containers and deployment services.
  • Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex.

Course Content

Module 1: Best Practices for Application Development

  • Code and environment management
  • Design and development of secure, scalable, reliable, loosely coupled application components and microservices
  • Continuous integration and delivery
  • Re-architecting applications for the cloud

Module 2: Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK

  • How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK
  • Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials

Module 3: Overview of Data Storage Options

  • Overview of options to store application data
  • Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner

Module 4: Best Practices for Using Cloud Datastore

  • Best practices related to the following: Queries, Built-in and composite indexes, Inserting and deleting data (batch operations), Transactions, Error handling
  • Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow
  • Lab: Store application data in Cloud Datastore

Module 5: Performing Operations on Buckets and Objects

  • Operations that can be performed on buckets and objects
  • Consistency model
  • Error handling

Module 6: Best Practices for Using Cloud Storage

  • Naming buckets for static websites and other uses
  • Naming objects (from an access distribution perspective)
  • Performance considerations
  • Setting up and debugging a CORS configuration on a bucket
  • Lab: Store files in Cloud Storage

Module 7: Securing Your Application

  • Cloud Identity and Access Management (IAM) roles and service accounts
  • User authentication by using Firebase Authentication
  • User authentication and authorization by using Cloud Identity-Aware Proxy
  • Lab: Authenticate users by using Firebase Authentication

Module 8: Using Google Cloud Pub/Sub to Integrate Components of Your Application

  • Topics, publishers, and subscribers
  • Pull and push subscriptions
  • Use cases for Cloud Pub/Sub
  • Lab: Develop a backend service to process messages in a message queue

Module 9: Adding Intelligence to Your Application

  • Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API.

Module 10: Using Cloud Functions for Event-Driven Processing

  • Key concepts such as triggers, background functions, HTTP functions
  • Use cases
  • Developing and deploying functions
  • Logging, error reporting, and monitoring

Module 11: Using Cloud Endpoint to Deploy APIs

  • Open API deployment configuration
  • Lab: Deploy an API for your application

Module 12: Debugging Your Application by Using Google Stackdriver

  • Stackdriver Debugger
  • Stackdriver Error Reporting
  • Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting

Module 13: Deploying an Application by Using Google Cloud Container Builder, Google Cloud Container Registry, and Google Cloud Deployment Manager

  • Creating and storing container images
  • Repeatable deployments with deployment configuration and templates
  • Lab: Use Deployment Manager to deploy a web application into Google App Engine Flex test and production environments

Module 14: Execution Environments for Your Application

  • Considerations for choosing an execution environment for your application or service: Google Compute Engine, Container Engine, App Engine Flex, Cloud Functions, Cloud Dataflow
  • Lab: Deploying your application on App Engine Flex

Module 15: Monitoring and Tuning Performance

  • Best practices and watchpoints for performance
  • Key concepts related to Stackdriver Trace and Stackdriver Monitoring
  • Detecting and resolving performance issues
  • Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance

Course Overview

Learn the fundamentals of API Design and the out-of-the-box capabilities offered by Google Cloud’s Apigee API Platform. This course features a combination of lectures, hands-on labs, and supplemental materials to show you how to design, build, secure, deploy, and manage API solutions.

Course Objectives

  • Identify the purpose and value of Google Cloud’s Apigee API Platform.
  • Develop a good understanding of Google Cloud’s Apigee API Platform terminology and organizational model.
  • Interact with Google Cloud’s Apigee API Platform.
  • Solve scenarios by leveraging APIs, recommended practices, and an API-first strategy.
  • Understand and put in practice the API lifecycle.
  • Identify capabilities available to secure, scale, and manage APIs and API products.

Course Overview

Azure OpenAI Service provides access to OpenAI’s powerful large language models such as ChatGPT, GPT, Codex, and Embeddings models.

These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio.

Course Objectives

Students will learn how to,

  • Get started with Azure OpenAI Service
  • Build natural language solutions with Azure OpenAI Service
  • Apply prompt engineering with Azure OpenAI Service
  • Generate code with Azure OpenAI Service
  • Generate images with Azure OpenAI Service
  • Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
  • Fundamentals of Responsible Generative AI

Course Content

Module 1 : Get started with Azure OpenAI Service

  • Create an Azure OpenAI Service resource and understand types of Azure OpenAI base models.
  • Use the Azure OpenAI Studio, console, or REST API to deploy a base model and test it in the Studio’s playgrounds.
  • Generate completions to prompts and begin to manage model parameters.

Module 2 : Build natural language solutions with Azure OpenAI Service

  • Integrate Azure OpenAI into your application
  • Differentiate between different endpoints available to your application
  • Generate completions to prompts using the REST API and language specific SDKs

Module 3 : Apply prompt engineering with Azure OpenAI Service

  • Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models’ performance.
  • Know how to design and optimize prompts to better utilize AI models.
  • Include clear instructions, request output composition, and use contextual content to improve the quality of the model’s responses.

Module 4 : Generate code with Azure OpenAI Service

  • Use natural language prompts to write code
  • Build unit tests and understand complex code with AI models
  • Generate comments and documentation for existing code

Module 5 : Generate images with Azure OpenAI Service

  • Describe the capabilities of DALL-E in the Azure openAI service
  • Use the DALL-E playground in Azure OpenAI Studio
  • Use the Azure OpenAI REST interface to integrate DALL-E image generation into your apps

Module 6 : Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service

  • Describe the capabilities of Azure OpenAI on your data
  • Configure Azure OpenAI to use your own data
  • Use Azure OpenAI API to generate responses based on your own data

Module 7: Fundamentals of Responsible Generative AI

  • Describe an overall process for responsible generative AI solution development
  • Identify and prioritize potential harms relevant to a generative AI solution
  • Measure the presence of harms in a generative AI solution
  • Mitigate harms in a generative AI solution
  • Prepare to deploy and operate a generative AI solution responsibly

Course Overview

Transform and load data, define semantic model relationships and calculations, create interactive visuals, and distribute reports using Power BI.

Course Content

Module 1: Get data in Power BI

You’ll learn how to retrieve data from a variety of data sources, including Microsoft Excel, relational databases, and NoSQL data stores. You’ll also learn how to improve performance while retrieving data.

  • Identify and connect to a data source
  • Get data from a relational database, like Microsoft SQL Server
  • Get data from a file, like Microsoft Excel
  • Get data from applications
  • Get data from Azure Analysis Services
  • Select a storage mode
  • Fix performance issues
  • Resolve data import errors

Module 2: Clean, transform, and load data in Power BI

Power Query has an incredible number of features that are dedicated to helping you clean and prepare your data for analysis. You’ll learn how to simplify a complicated model, change data types, rename objects, and pivot data. You’ll also learn how to profile columns so that you know which columns have the valuable data that you’re seeking for deeper analytics.

  • Resolve inconsistencies, unexpected or null values, and data quality issues.
  • Apply user-friendly value replacements.
  • Profile data so you can learn more about a specific column before using it.
  • Evaluate and transform column data types.
  • Apply data shape transformations to table structures.
  • Combine queries.
  • Apply user-friendly naming conventions to columns and queries.
  • Edit M code in the Advanced Editor.

Module 3: Design a semantic model in Power BI

The process of creating a complicated semantic model in Power BI is straightforward. If your data is coming in from more than one transactional system, before you know it, you can have dozens of tables that you have to work with. Building a great semantic model is about simplifying the disarray. A star schema is one way to simplify a semantic model, and you learn about the terminology and implementation of them in this module. You will also learn about why choosing the correct data granularity is important for performance and usability of your Power BI reports. Finally, you learn about improving performance with your Power BI semantic models.

  • Create common date tables
  • Configure many-to-many relationships
  • Resolve circular relationships
  • Design star schemas

Module 4: Add measures to Power BI Desktop models

In this module, you’ll learn how to work with implicit and explicit measures. You’ll start by creating simple measures, which summarize a single column or table. Then, you’ll create more complex measures based on other measures in the model. Additionally, you’ll learn about the similarities of, and differences between, a calculated column and a measure.

  • Determine when to use implicit and explicit measures.
  • Create simple measures.
  • Create compound measures.
  • Create quick measures.
  • Describe similarities of, and differences between, a calculated column and a measure.

Module 5: Add calculated tables and columns to Power BI Desktop models

By the end of this module, you’ll be able to add calculated tables and calculated columns to your semantic model. You’ll also be able to describe row context, which is used to evaluated calculated column formulas. Because it’s possible to add columns to a table using Power Query, you’ll also learn when it’s best to create calculated columns instead of Power Query custom columns.

  • Create calculated tables.
  • Create calculated columns.
  • Identify row context.
  • Determine when to use a calculated column in place of a Power Query custom column.
  • Add a date table to your model by using DAX calculations.

Module 6: Design Power BI reports

Because Power BI includes more than 30 core visuals, it can be challenging for a beginner to select the correct visual. This module will guide you through selecting the most appropriate visual type to meet your design and report layout requirements.

  • Learn about the structure of a Power BI report.
  • Learn about report objects.
  • Select the appropriate visual type to use.

Module 7: Configure Power BI report filters

Report filtering is a complex topic because many techniques are available for filtering a Microsoft Power BI report. However, with complexity comes control, allowing you to design reports that meet requirements and expectations. Some filtering techniques apply at design time, while others are relevant at report consumption time (in reading view). What matters is that your report design allows report consumers to intuitively narrow down to the data points that interest them.

  • Design reports for filtering.
  • Design reports with slicers.
  • Design reports by using advanced filtering techniques.
  • Apply consumption-time filtering.
  • Select appropriate report filtering techniques.

Module 8: Create and manage workspaces in Power BI

Learn how to navigate the Power BI service, create and manage workspaces and related items, and distribute reports to users.

  • Create and manage Power BI workspaces and items.
  • Distribute a report or dashboard.
  • Monitor usage and performance.
  • Recommend a development lifecycle strategy.
  • Troubleshoot data by viewing its lineage.
  • Configure data protection.

Module 9:

Manage semantic models in Power BI

With Microsoft Power BI, you can use a single semantic model to build many reports. Reduce your administrative overhead even more with scheduled semantic model refreshes and resolving connectivity errors.

  • Use a Power BI gateway to connect to on-premises data sources.
  • Configure a scheduled refresh for a semantic model.
  • Configure incremental refresh settings.
  • Manage and promote semantic models.
  • Troubleshoot service connectivity.
  • Boost performance with query caching (Premium).

Course Overview

Learn how to gather information from API documentation and perform HTTP operations in an ASP.NET Core Razor Pages web app.

Course Objectives

  • Interact with an ASP.NET Core minimal API
  • Implement HTTP operations in ASP.NET Razor Pages
  • Render API responses in ASP.NET Core Razor Pages

Course Content

Module 1: Interact with an ASP.NET Core minimal API

  • Learn how APIs are implemented in ASP.NET Core, and how to use API documentation to learn the APIs requirements.

Module 2: Implement HTTP operations in ASP.NET Razor Pages

  • Learn how to implement HTTP clients based on HttpClient and IHttpClientFactory. And how to implement code to perform HTTP operations in ASP.NET Core Razor Pages.

Module 3: Render API responses in ASP.NET Core Razor Pages

  • Learn how to render API responses in ASP.NET Core Razor Pages and perform HTTP operations by using page handlers.

Course Overview

This course teaches Azure Solution Architects how to design infrastructure solutions. Course topics cover governance, compute, application architecture, storage, data integration, authentication, networks, business continuity, and migrations. The course combines lecture with case studies to demonstrate basic architect design principles.

Course Objectives

  • AZ-305 Microsoft Azure Architect Design Prerequisites
  • AZ-305: Design identity, governance, and monitor solutions
  • AZ-305: Design business continuity solutions
  • AZ-305: Design data storage solutions
  • AZ-305: Design infrastructure solutions
  • Build great solutions with the Microsoft Azure Well-Architected Framework
  • Accelerate cloud adoption with the Microsoft Cloud Adoption Framework for Azure

Course Content

AZ-305 Microsoft Azure Architect Design Prerequisites

  • Describe the core architectural components of Azure
  • Describe Azure compute and networking services
  • Describe Azure storage services
  • Describe Azure identity, access, and security
  • Microsoft Cloud Adoption Framework for Azure
  • Introduction to the Microsoft Azure Well-Architected Framework

AZ-305: Design identity, governance, and monitor solutions

  • Design governance
  • Design authentication and authorization solutions
  • Design a solution to log and monitor Azure resources

AZ-305: Design business continuity solutions

  • Describe high availability and disaster recovery strategies
  • Design a solution for backup and disaster recovery

AZ-305: Design data storage solutions

  • Design a data storage solution for non-relational data
  • Design a data storage solution for relational data
  • Design data integration

AZ-305: Design infrastructure solutions

  • Design an Azure compute solution
  • Design an application architecture
  • Design network solutions
  • Design migrations

Build great solutions with the Microsoft Azure Well-Architected Framework

  • Introduction to the Microsoft Azure Well-Architected Framework
  • Microsoft Azure Well-Architected Framework – Cost Optimization
  • Microsoft Azure Well-Architected Framework – Operational excellence
  • Microsoft Azure Well-Architected Framework – Performance efficiency
  • Microsoft Azure Well-Architected Framework – Reliability
  • Microsoft Azure Well-Architected Framework – Security

Accelerate cloud adoption with the Microsoft Cloud Adoption Framework for Azure

  • Getting started with the Microsoft Cloud Adoption Framework for Azure
  • Prepare for successful cloud adoption with a well-defined strategy
  • Prepare for cloud adoption with a data-driven plan
  • Choose the best Azure landing zone to support your requirements for cloud operations
  • Use the Cloud Adoption Framework Migrate methodology to migrate your workload to the cloud
  • Address tangible risks with the Govern methodology of the Cloud Adoption Framework for Azure
  • Ensure stable operations and optimization across all supported workloads deployed to the cloud
  • Innovate applications by using Azure cloud technologies
  • Prepare for cloud security by using the Microsoft Cloud Adoption Framework for Azure

Course Overview

This course teaches Network Engineers how to design, implement, and maintain Azure networking solutions. This course covers the process of designing, implementing, and managing core Azure networking infrastructure, Hybrid Networking connections, load balancing traffic, network routing, private access to Azure services, network security and monitoring. Learn how to design and implement a secure, reliable, network infrastructure in Azure and how to establish hybrid connectivity, routing, private access to Azure services, and monitoring in Azure.

Course Objectives

Designing and Implementing Microsoft Azure Networking Solutions

Course Content

AZ-700 Designing and Implementing Microsoft Azure Networking Solutions

  • Introduction to Azure Virtual Networks
  • Design and implement hybrid networking
  • Design and implement Azure ExpressRoute
  • Load balance non-HTTP(S) traffic in Azure
  • Load balance HTTP(S) traffic in Azure
  • Design and implement network security
  • Design and implement private access to Azure Services
  • Design and implement network monitoring

Course Overview

Exclusive – Learn to build AI infused applications that leverage Azure AI Services, Azure AI Search, and Azure OpenAI.

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. The course will use C# or Python as the programming language.

Course Objectives

Students will learn to:

  • Get started with Azure AI Services
  • Create computer vision solutions with Azure AI Vision
  • Develop natural language processing solutions with Azure AI Services
  • Implement knowledge mining with Azure AI Search
  • Develop solutions with Azure AI Document Intelligence
  • Develop Generative AI solutions with Azure OpenAI Service

Course Content

Module 1: Get started with Azure AI Services

  • Prepare to develop AI solutions on Azure
  • Create and consume Azure AI services
  • Secure Azure AI services
  • Monitor Azure AI services
  • Deploy Azure AI services in containers

Module 2: Create computer vision solutions with Azure AI Vision

  • Analyze images
  • Image classification with custom Azure AI Vision models
  • Detect, analyze, and recognize faces
  • Read Text in images and documents with the Azure AI Vision Service
  • Analyze video

Module 3: Develop natural language processing solutions with Azure AI Services

  • Analyze text with Azure AI Language
  • Create question answering solutions with Azure AI Language
  • Build a conversational language understanding model
  • Create a custom text classification solution
  • Custom named entity recognition
  • Translate text with Azure AI Translator service
  • Create speech-enabled apps with Azure AI services
  • Translate speech with the Azure AI Speech service

Module 4 : Implement knowledge mining with Azure AI Search

  • Create an Azure AI Search solution
  • Create a custom skill for Azure AI Search
  • Create a knowledge store with Azure AI Search
  • Enrich your data with Azure AI Language
  • Implement advanced search features in Azure AI Search
  • Build an Azure Machine Learning custom skill for Azure AI Search
  • Search data outside the Azure platform in Azure AI Search using Azure Data Factory
  • Maintain an Azure AI Search solution
  • Perform search re-ranking with semantic ranking in Azure AI Search
  • Perform vector search and retrieval in Azure AI Search

Module 5: Develop solutions with Azure AI Document Intelligence

  • Plan an Azure AI Document Intelligence solution
  • Use prebuilt Document intelligence models
  • Extract data from forms with Azure Document intelligence
  • Create a composed Document intelligence model
  • Build a Document intelligence custom skill for Azure AI search

Module 6: Develop Generative AI solutions with Azure OpenAI Service

  • Get started with Azure OpenAI Service
  • Build natural language solutions with Azure OpenAI Service
  • Apply prompt engineering with Azure OpenAI Service
  • Generate code with Azure OpenAI Service
  • Generate images with Azure OpenAI Service
  • Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
  • Fundamentals of Responsible Generative AI