Overview
Take your AI skills to the next level with the Microsoft Certified: Azure AI Engineer Associate credential.
Azure AI solutions empower developers and organizations to build, deploy, and operationalize AI products and services at scale with its comprehensive suite of pre-built and customizable AI services for vision, speech, language, knowledge, and search. By getting skilled on the latest AI technology Azure offers, you can help your organization automate tasks, improve customer service, gain insights from data, and make better decisions faster.
Gain mastery of AI and skills needed to embed artificial intelligence capabilities into apps, including Cognitive Services, natural language processing, and conversational AI with the Azure AI Engineer Associate certification.
The AI-102T00 Designing and Implementing an Azure AI Solution training is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language.
Lead the era of AI with Microsoft. Power your organization’s AI transformation with Microsoft Cloud. The AI you can trust.
Reimagine Your Workflow With Azure AI Solutions – FREE virtual training event
Date: February 13, 2025, Mode: Virtual
Explore how Azure AI tools like Foundry and Copilot Studio can automate workflows, enhance productivity, and drive innovation. Gain hands-on insights from expert-led sessions—all for free.
Skills Covered
- Describe considerations for AI-enabled application development
- Create, configure, deploy, and secure Azure Cognitive Services
- Develop applications that analyze text
- Develop speech-enabled applications
- Create applications with natural language understanding capabilities
- Create QnA applications
- Create conversational solutions with bots
- Use computer vision services to analyze images and videos
- Create custom computer vision models
- Develop applications that detect, analyze, and recognize faces
- Develop applications that read and process text in images and documents
- Create intelligent search solutions for knowledge mining
Who Should Attend
Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.
This AI-102T00 training course prepares students for the Microsoft Certified: Azure AI Engineer Associate certification. The AI-102 exam measures your ability to accomplish the following technical tasks: plan and manage an Azure Cognitive Services solutions; implement Computer Vision solutions; implement natural language processing solutions; implement knowledge mining solutions; and implement conversational AI solutions.
As an Azure AI engineer, you analyze requirements for AI solutions, recommend the appropriate tools and technologies, and design and implement AI solutions that meet scalability and performance requirements. Your responsibilities include translating the vision from solution architects and working with data scientists, data engineers, IoT specialists, and software developers to build complete end-to-end solutions. If this describes your workday, this could be the certification for you.
Course Curriculum
Prerequisites
Before attending this course, students must have:
- Knowledge of Microsoft Azure and ability to navigate the Azure portal
- Knowledge of either C# or Python
- Familiarity with JSON and REST programming semantics
- To gain C# or Python skills, complete the free Take your first steps with C# or Take your first steps with Python learning path before attending the course.
- If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the Azure AI Fundamentals certification before taking this one.
Download Course Syllabus
Course Modules
As an aspiring Azure AI Engineer, you should understand core concepts and principles of AI development, and the capabilities of Azure services used in AI solutions.
Learning objectives
After completing this module, you will be able to:
- Define artificial intelligence
- Understand AI-related terms
- Understand considerations for AI Engineers
- Understand considerations for responsible AI
- Understand capabilities of Azure Machine Learning
- Understand capabilities of Azure AI Services
- Understand capabilities of Azure OpenAI Service
- Understand capabilities of Azure AI Search
Azure AI services enable developers to easily add AI capabilities into their applications. Learn how to create and consume these services.
Learning objectives
After completing this module, you’ll able to:
- Create Azure AI services resources in an Azure subscription.
- Identify endpoints, keys, and locations required to consume an Azure AI services resource.
- Use a REST API and an SDK to consume Azure AI services.
Prerequisites
Before starting this module, you should have:
- Experience of provisioning and managing Azure services in the Azure portal.
- A knowledge of C# or Python.
Securing Azure AI services can help prevent data loss and privacy violations for user data that may be a part of the solution.
Learning objectives
After completing this module, you will know how to:
- Consider authentication for Azure AI services
- Manage network security for Azure AI services
Prerequisites
- Active Azure subscription
- Ability to navigate the Azure portal
- Understanding of networking concepts
Azure AI services enable you to integrate artificial intelligence into your applications and services. It’s important to be able to monitor Azure AI Services in order to track utilization, determine trends, and detect and troubleshoot issues.
Learning objectives
After completing this module, you will be able to:
- Monitor Azure AI services costs.
- Create alerts and view metrics for Azure AI services.
- Manage Azure AI services diagnostic logging.
Prerequisites
Before starting this module, you should:
- Be familiar with Azure services and the Azure portal.
- Have experience of provisioning Azure AI services resources.
Learn about Container support in Azure AI services allowing the use of APIs available in Azure and enable flexibility in where to deploy and host the services with Docker containers.
Learning objectives
After completing this module, learners will be able to:
- Create containers for reuse
- Deploy to a container and secure a container
- Consume Azure AI services from a container
Prerequisites
- Learners should have an Azure account and be familiar with navigating the Azure portal.
- Learners should also have experience with Docker and containers.
With the Azure AI Vision service, you can use pre-trained models to analyze images and extract insights and information from them.
Learning objectives
After completing this module, you’ll be able to:
- Provision an Azure AI Vision resource
- Analyze an image
- Generate a smart-cropped thumbnail
Prerequisites
Before starting this module, you should already have:
- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path first.
Image classification is used to determine the main subject of an image. You can use the Azure AI Custom Vision services to train a model that classifies images based on your own categorizations.
Learning objectives
After completing this module, you will be able to:
- Provision Azure resources for Azure AI Custom Vision
- Understand image classification
- Train an image classifier
Prerequisites
Before starting this module, you should already have:
- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path first.
The ability for applications to detect human faces, analyze facial features and emotions, and identify individuals is a key artificial intelligence capability.
Learning objectives
After completing this module, you will be able to:
- Identify options for face detection, analysis, and identification
- Understand considerations for face analysis
- Detect faces with the Azure AI Vision service
- Understand capabilities of the Face service
- Compare and match detected faces
- Implement facial recognition
Prerequisites
Before starting this module, you should already have:
- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path first.
Azure’s AI Vision service uses algorithms to process images and return information. This module teaches you how to use the Image Analysis API for optical character recognition (OCR).
Learning objectives
In this module, you’ll learn how to:
- Read text from images using OCR
- Use the Azure AI Vision service Image Analysis with SDKs and the REST API
- Develop an application that can read printed and handwritten text
Prerequisites
To complete this module, you’ll need:
- An active Azure account
- Knowledge of Azure portal navigation
- Knowledge of at least one programming language (C#, Python)
Azure Video Indexer is a service to extract insights from video, including face identification, text recognition, object labels, scene segmentations, and more.
Learning objectives
After completing this module, you’ll be able to:
- Describe Azure Video Indexer capabilities
- Extract custom insights
- Use Azure Video Indexer widgets and APIs
Prerequisites
Before starting this module, you should already have:
- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path first.
The Azure AI Language service enables you to create intelligent apps and services that extract semantic information from text.
Learning objectives
In this module, you’ll learn how to use the Azure AI Language service to:
- Detect language from text
- Analyze text sentiment
- Extract key phrases, entities, and linked entities
Prerequisites
Before starting this module, you’ll need
- Familiarity with Microsoft Azure and the Azure portal.
- Experience programming with C# or Python.
The question answering capability of the Azure AI Language service makes it easy to build applications in which users ask questions using natural language and receive appropriate answers.
Learning objectives
After completing this module, you will be able to:
- Understand question answering and how it compares to language understanding
- Create, test, publish and consume a knowledge base
- Implement multi-turn conversation and active learning
- Create a question answering bot to interact with using natural language
Prerequisites
Before starting this module, you should already have:
- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path first.
The Azure AI Language conversational language understanding service (CLU) enables you to train a model that apps can use to extract meaning from natural language.
Learning objectives
After completing this module, you’ll be able to:
- Provision Azure resources for Azure AI Language resource
- Define intents, utterances, and entities
- Use patterns to differentiate similar utterances
- Use pre-built entity components
- Train, test, publish, and review an Azure AI Language model
Prerequisites
Before starting this module, you should already have:
- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path first.
The Azure AI Language service enables processing of natural language to use in your own app. Learn how to build a custom text classification project.
Learning objectives
After completing this module, you’ll be able to:
- Understand types of classification projects
- Build a custom text classification project
- Tag data, train, and deploy a model
- Submit classification tasks from your own app
Prerequisites
Before starting this module, you should be familiar with:
- The Azure portal
- Familiarity with Azure AI Services
- General programming techniques
Build a custom entity recognition solution to extract entities from unstructured documents
Learning objectives
After completing this module, you’ll be able to:
- Understand custom named entities and how they’re labeled.
- Build a Language service project.
- Label data, train, and deploy an entity extraction model.
- Submit extraction tasks from your own app.
Prerequisites
Before starting this module, you should be familiar with:
- The Azure portal
- General functionality of Azure AI Services
- General programming technique
The Translator service enables you to create intelligent apps and services that can translate text between languages.
Learning objectives
After completing this module, you’ll be able to:
- Provision a Translator resource
- Understand language detection, translation, and transliteration
- Specify translation options
- Define custom translations
Prerequisites
Before starting this module, you need
- Familiarity with Microsoft Azure and the Azure portal.
- Experience programming with C# or Python.
The Azure AI Speech service enables you to build speech-enabled applications. This module focuses on using the speech-to-text and text to speech APIs, which enable you to create apps that are capable of speech recognition and speech synthesis.
Learning objectives
In this module, you’ll learn how to:
- Provision an Azure resource for the Azure AI Speech service
- Use the Azure AI Speech to text API to implement speech recognition
- Use the Text to speech API to implement speech synthesis
- Configure audio format and voices
- Use Speech Synthesis Markup Language (SSML)
Prerequisites
Before starting this module, you should:
- Be familiar with Azure services and the Azure portal
- Have experience programming with C# or Python
Translation of speech builds on speech recognition by recognizing and transcribing spoken input in a specified language, and returning translations of the transcription in one or more other languages.
Learning objectives
In this module, you will learn how to:
- Provision Azure resources for speech translation.
- Generate text translation from speech.
- Synthesize spoken translations.
Prerequisites
Before starting this module, you should:
- Be familiar with Azure services and the Azure portal.
- Have experience programming with C# or Python.
- Have experience of using the Azure AI Speech service to transcribe speech to text.
Unlock the hidden insights in your data with Azure AI Search.
Learning objectives
In this module you’ll learn how to:
- Create an Azure AI Search solution
- Develop a search application
Prerequisites
- Familiarity with Microsoft Azure
- Application development experience with C# or Python
Use the power of artificial intelligence to enrich your data and find new insights.
Learning objectives
In this module you will learn how to:
- Implement a custom skill for Azure AI Search
- Integrate a custom skill into an Azure AI Search skillset
- Familiarity with Microsoft Azure
- Application development experience with C# or Python
- Experience of building a basic search solution with Azure AI Search. If you have not completed the Create an Azure AI Search solution module, you should do so before starting this module.
Persist the output from an Azure AI Search enrichment pipeline for independent analysis or downstream processing.
Learning objectives
In this module you’ll learn how to:
- Create a knowledge store from an Azure AI Search pipeline
- View data in projections in a knowledge store
Prerequisites
- Familiarity with Microsoft Azure
- Application development experience with C# or Python
- Experience of creating an enrichment pipeline with Azure AI Search If you haven’t completed the Create an Azure AI Search solution module, do so before starting this module.
Learn how to use Azure AI Document Intelligence to build solutions that analyze forms and output data for storage or further processing.
Learning objectives
In this module, you will learn to:
- Describe the components of an Azure AI Document Intelligence solution.
- Create and connect to Azure AI Document Intelligence resources in Azure.
- Choose whether to use a prebuilt, custom, or composed model.
Prerequisites
- Basic understanding of Azure AI Document Intelligence
Learn what data you can analyze by choosing prebuilt Forms Analyzer models and how to deploy these models in a Form Analyzer solution.
Learning objectives
In this module, you will learn to:
- Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
- Analyze forms by using the General Document, Read, and Layout models.
- Analyze forms by using financial, ID, and tax prebuilt models
Prerequisites
- Basic understanding of Form Recognizer
Azure Document Intelligence uses machine learning technology to identify and extract key-value pairs and table data from form documents with accuracy, at scale. This module teaches you how to use the Azure Document Intelligence Azure AI service.
Learning objectives
In this module, you’ll learn how to:
- Identify how Azure Document Intelligence’s layout service, prebuilt models, and custom service can automate processes
- Use Azure Document Intelligence’s Optical Character Recognition (OCR) capabilities with SDKs, REST API, and Azure Document Intelligence Studio
- Develop and test custom models
Prerequisites
To complete this module, you’ll need:
- An active Azure account
- Knowledge of Azure portal navigation
- Knowledge of at least one programming language (C#, Python)
This module provides engineers with the skills to begin building an Azure OpenAI Service solution.
Learning objectives
By the end of this module, you’ll be able to:
- 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.
- Familiarity with Azure and the Azure portal.
- An understanding of generative AI. You can learn more with Introduction to Azure OpenAI Service.
This module provides engineers with the skills to begin building apps that integrate with the Azure OpenAI Service.
Learning objectives
By the end of this module, you’ll be able to:
- 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
Prerequisites
- Familiarity with Azure and the Azure portal.
- An understanding of generative AI. You can learn more with Introduction to Azure OpenAI Service.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path before starting this one.
Prompt engineering in Azure OpenAI is a technique that involves designing prompts for natural language processing models. This process improves accuracy and relevancy in responses, optimizing the performance of the model.
Learning objectives
By the end of this module, you’ll be able to:
- 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.
Prerequisites
- Familiarity with Azure and the Azure portal.
- An understanding of generative AI. You can learn more with Introduction to Azure OpenAI Service.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path before starting this one.
- Before starting this module, we recommend you complete the previous modules in the Develop AI solutions with Azure OpenAI learning path.
This module shows engineers how to use the Azure OpenAI Service to generate and improve code.
Learning objectives
By the end of this module, you’ll be able to:
- Use natural language prompts to write code
- Build unit tests and understand complex code with AI models
- Generate comments and documentation for existing code
Prerequisites
- Familiarity with Azure and the Azure portal.
- An understanding of generative AI. You can learn more with Introduction to Azure OpenAI Service.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path before starting this one.
- Before starting this module, we recommend you complete the previous modules in the Develop AI solutions with Azure OpenAI learning path.
The Azure OpenAI service includes the DALL-E model, which you can use to generate original images based on natural language prompts.
Learning objectives
By the end of this module, you’ll be able to:
- 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
Prerequisites
Before starting this module, you should be familiar with the Azure OpenAI service. Consider completing the Get started with Azure OpenAI Service module before starting this one.
Azure OpenAI on your data allows developers to use supported AI chat models that can reference specific sources of data to ground the response.
Learning objectives
By the end of this module, you’ll be able to:
- 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
Prerequisites
- Familiarity with Azure and the Azure portal.
- An understanding of generative AI. You can learn more with Introduction to Azure OpenAI Service.
- Before starting this module, we recommend you complete the previous modules in the Develop AI solutions with Azure OpenAI learning path.
Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.
Learning objectives
By the end of this module, you’ll be able to:
- 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
Prerequisites
Before starting this module, you should be familiar with the Azure OpenAI service. Consider completing the Introduction to Azure OpenAI Service module before starting this one.
Request More Information
Training Options
- VILT: Virtual Instructor-Led Training
- ILT: Instructor-Led Training
Exam & Certification
Microsoft Certified: Azure AI Engineer Associate.
The Azure AI Engineer Associate certification validates that you have subject matter expertise using cognitive services, machine learning, and knowledge mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, and conversational AI.
You can earn the certification by passing Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution, which has replaced Exam AI-100. The updated exam focuses more on practical engineering and less on solution architecture, in keeping with the current skills for the role.
Future growth in many industries will be empowered by AI and the engineers who work with it. Whether you’re building mission-critical solutions to understand speech, make predictions, or analyze images, or you’re using bots to engage customers and empower employees, there are a lot of career doors to open. The Azure AI Engineer certification is a great opportunity to prove your skills and worth to current and future employers.
Training & Certification Guide
As a Microsoft Azure AI engineer, you build, manage, and deploy AI solutions that leverage Azure AI.
Your responsibilities include participating in all phases of AI solutions development, including:
- Requirements definition and design
- Development
- Deployment
- Integration
- Maintenance
- Performance tuning
- Monitoring
You work with solution architects to translate their vision. You also work with data scientists, data engineers, Internet of Things (IoT) specialists, infrastructure administrators, and other software developers to:
- Build complete and secure end-to-end AI solutions.
- Integrate AI capabilities in other applications and solutions.
As an Azure AI engineer, you have experience developing solutions that use languages such as:
- Python
- C#
You should be able to use Representational State Transfer (REST) APIs and SDKs to build secure image processing, video processing, natural language processing, knowledge mining, and generative AI solutions on Azure. You should:
- Understand the components that make up the Azure AI portfolio and the available data storage options.
- Be able to apply responsible AI principles.
This exam measures your ability to accomplish the following technical tasks: plan and manage an Azure AI solution; implement decision support solutions; implement computer vision solutions; implement natural language processing solutions; implement knowledge mining solutions and document intelligence solutions; and implement generative AI solutions.
Skills measured:
- Plan and manage an Azure AI solution (15–20%)
- Implement decision support solutions (10–15%)
- Implement computer vision solutions (15–20%)
- Implement natural language processing solutions (30–35%)
- Implement knowledge mining and document intelligence solutions (10–15%)
Implement generative AI solutions (10–15%)
When you earn a certification or learn a new skill, it’s an accomplishment worth celebrating with your network. It often takes less than a minute to update your LinkedIn profile and share your achievements, highlight your skills, and help boost your career potential. Here’s how:
- If you’ve earned a certification already, follow the instructions in the congratulations email you received. Or find your badge on your Certification Dashboard, and follow the instructions there to share it. (You’ll be transferred to the Acclaim website.)
- To add specific skills, visit your LinkedIn profile and update the Skills and endorsements section. Tip: We recommend that you choose skills listed in the skills outline guide for your certification.
If you’ve already earned your Azure AI Engineer Associate certification, but it’s expiring in the near future, we’ve got good news. You’ll soon be able to renew your current certifications by passing a free renewal assessment on Microsoft Learn—anytime within six months before your certification expires. For more details, please read our blog post, Stay current with in-demand skills through free certification renewals.
AI Mastery Program: Learn AI with Microsoft in 2024
Artificial Intelligence (AI) has been one of the hottest topics in the tech industry for the past decade. With its rapid advancements and potential to impact our daily lives, learning AI has become a highly sought-after skill.
We are thrilled to announce the launch of our Microsoft AI Mastery Program in 2024, made possible through our partnership with Microsoft, a renowned leader in AI technology. This program is tailored for individuals and tech professionals passionate about acquiring and mastering the fundamental and advanced principles of AI.
Top AI Skills Malaysia Needs Today: Advance Your IT Career in 2024
In the quest for AI-driven innovation, Malaysia seeks sharp minds with the right IT skills. Data science, machine learning—these are the tools in high demand. This article examines the AI skills Malaysia needs, the industries driving this change, and how to develop these skills to enhance your career trajectory.
Azure Strategy & Implementation Guide
Get a step-by-step introduction to using Azure for your cloud infrastructure with this Pack e-book. Read the latest edition of the Azure Strategy and Implementation Guide for detailed guidance on how to create a successful cloud adoption strategy with new innovations, capabilities, and security features from Microsoft Azure.
Microsoft Azure SQL Jumpstart Guide
Find out how to get started launching your first Azure SQL database or find ways to make your existing SQL database work harder. Download the Azure SQL Jumpstart Guide for detailed instructions and in-depth insights to help you make your Azure SQL deployment, migration, or enhancement run smoothly.
Low-code Application Development – Microsoft PowerApps and Azure
Build production-ready apps faster with a low-code environment. Quickly stand up your applications with Power Apps and get more time to apply your technical expertise to extending and optimizing those apps in Azure.
Azure Cloud Native Architecture Mapbook
Grow your cloud architecture skills with guidance from Azure Experts. Go beyond developing cloud-native applications to planning and implementing cloud application infrastructure. In this free e-book from Packt Publishing, you’ll find best practices for infrastructure design and patterns for building a complete solution.
Windows Virtual Desktop Security
Find out how to secure your Windows Virtual Desktop environment when migrating your virtual desktop infrastructure (VDI) to Azure. Read this security handbook to get technical hands-on guidance on how to help protect your apps and data in your Windows Virtual Desktop deployment.
Discover how to get more value from your on premises Windows Server and SQL Server investments and move some or all of your workloads to the cloud using your existing skills. See how to start using the cloud to support new ways of doing business and help ensure business continuity even if you need to keep some of your IT assets on-premises due to regulatory or data governance requirements.
Discover how to build highly scalable applications using containers and how to deploy and manage those containers at scale with Kubernetes on Azure. Read the completely reviewed and updated Packet e-book, Hands-On Kubernetes on Azure, Third Edition and discover what’s new, including security enhancements, continuous integration and continuous delivery (CI/CD) automation, and the latest supported technologies. Gain insight into building reliable applications in the new foreword by Kubernetes co-founder Brendan Burns.
Azure Synapse Analytics Proof of Concept Playbook
Learn how to perform a proof of concept efficiently and economically with Azure Synapse Analytics. Read the Azure Synapse Analytics Proof of Concept Playbook to understand the key concepts involved in deploying data warehousing, data lake, and big data workloads with Azure Synapse and get the evidence you need to make the case for implementation at your organization.
Spend less time managing server infrastructure and more time building great apps. Get your solutions to market faster using Azure Functions, a fully managed compute platform for processing data, integrating systems, and building simple APIs and microservices. The Azure Serverless Computing Cookbook will, through the development of basic back-end wep API that performs simple operations, helps you understand how to persist data in Azure Storage services.
Frequently Asked Questions
Future growth in many industries will be empowered by AI and the engineers who work with it. Whether you’re building mission-critical solutions to understand speech, make predictions, or analyze images, or you’re using bots to engage customers and empower employees, there are a lot of career doors to open. The Azure AI Engineer certification is a great opportunity to prove your skills and worth to current and future employers.
Earning a Microsoft Certification is globally recognized and industry-endorsed evidence of mastering real world skills. It shows you demonstrate proficiency in keeping pace with technology. It’s a career move that yields many positive results.
Getting a Microsoft Certification is also a great way to break into the tech industry. A Microsoft Certification immediately confers a level of authority and expertise, especially helpful for someone new to the industry.
The number of questions on a certification exam is subject to change as Microsoft make updates to ensure it aligns with current changes in the technology and job role. Most Microsoft Certification exams typically contain between 40-60 questions; and around 60-140 minutes.
Starting June 30 2021, all newly earned role-based and specialty certifications will be valid for one year from the date the certification was earned.
To stay up to date, IT pros are constantly learning and adding skills. The IDC study concluded that Microsoft Learning Partners such as Trainocate Malaysia which won the Microsoft Learning Partner of the Year 2021 award are well positioned to help organizations achieve their business and learning goals. The IT leaders who were surveyed found the most value from a Learning Partner that provides:
- An end-to-end solution which starts with identifying skill gaps, simplifies the learning experience, and finishes by evaluating how well the Learning Partner met the organization goals.
- Scale, flexibility, and speed to train teams of any size, in any location, amid changing circumstances.
- Value-added services, such as hands-on labs, classroom training, and custom content that help the skills development program succeed.
- High-quality content and delivery, meaning accurate, relevant courseware, top-notch instructors, and a path to certification, if needed.
AI-102-AO: Spatial Analysis
This Session will uncover how to use Spatial Analysis technology responsibly and how our AI technology works, and the choices system owners can make that influence system performance and behavior.
AZ-204T00: Developing Solutions for Microsoft Azure
This course teaches developers how to create end-to-end solutions in Microsoft Azure. Students will learn how to implement Azure compute solutions, create Azure Functions, implement and manage web apps, develop solutions utilizing Azure storage, implement authentication and authorization, and secure their solutions by using KeyVault and Managed Identities.
AZ-220T00: Microsoft Azure IoT Developer
This Microsoft Azure IoT Developer course provides students with the skills and knowledge required to successfully create and maintain the cloud and edge portions of an Azure IoT solution.
AZ-220-AO: IoT Plug and Play
This session will explore how IoT Plug and Play enables solution builders to integrate IoT devices with their solutions without any manual configuration. And how a device uses to advertise its capabilities to an IoT Plug and Play-enabled application.
AZ-305T00: Designing Microsoft Azure Infrastructure Solutions
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.
AZ-400T00: Designing and Implementing Microsoft DevOps solutions
This course provides the knowledge and skills to design and implement DevOps processes and practices. Students will learn how to plan for DevOps, use source control, scale Git for an enterprise, consolidate artifacts, design a dependency management strategy, manage secrets, implement continuous integration, implement a container build strategy, design a release strategy, set up a release management workflow,
