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Designing & Implementing a Microsoft Azure AI Solution (AI-102T00) Certification Training
The online instructor-led AI-102T00: Designing and Implementing a Microsoft Azure AI Solution training in Qatar teaches professionals how to implement practical skills needed for designing AI solutions and managing their deployment through Microsoft Azure.
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AI-102T00 Certification Training
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Course Overview
The AI-102T00: Designing and Implementing a Microsoft Azure AI Solution training in Qatar teaches professionals to build and sustain AI-powered applications using Microsoft Azure AI services. The training delivers a systematic framework that helps learners build expandable AI systems that combine machine learning capabilities with enterprise-level responsible AI requirements.
The training includes instruction about Azure AI tools that cover computer vision together with natural language processing and conversational AI and knowledge mining capabilities. Through the combination of Azure Cognitive Services with Azure Machine Learning and Azure Bot Services the participants will develop intelligent applications.
The program furnishes two vital elements that combine security practices for AI with governance models to help professionals create trustworthy ethical AI solutions.
The training enables learners to develop deployable AI model integration methods that facilitate automated workflow deployment on Azure AI platforms for cloud application combination. Learners need to understand how Azure Machine Learning trains models and evaluates and optimizes them before deploying them in order to achieve basic program learning objectives.
The program aims for AI engineers and data scientists and software developers who need to develop their AI design skills for their AI-102 certification pursuit. The training provides essential business information about Azure AI services to IT professionals who seek this knowledge.
Participants obtain complete Microsoft Azure AI expertise through the program to build intelligent automated data-driven solutions.
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Course Objectives
- Understand the core components of Microsoft Azure AI, including cognitive services, machine learning, and knowledge discovery.
- Master the design, development, and deployment of AI models using Azure Machine Learning and Cognitive Services.
- Apply computer vision, natural languag processing (NLP), and conversational AI to real-world applications.
- Utilize Azure Bot Services to create and integrate AI-powered chatbots for automated interactions.
- Optimize AI models through training, evaluation, and fine-tuning for improved accuracy and efficiency.
- Implement responsible AI strategies, focusing on bias detection, fairness, transparency, and security compliance.
- Automate AI workflows using Azure Logic Apps, Azure Functions, and AI-driven automation solutions.
- Integrate AI models seamlessly with cloud-based applications and enterprise systems.
- Gain practical experience with hands-on labs, case studies, and real-world AI solution development.
- Prepare effectively for the AI-102 certification exam, showcasing expertise in designing and deploying Azure AI solutions.
Audience
- DevOps Engineers
- AI Engineers
- AI-102 Certification Candidates
- Software Developers
- Data Scientists
- Cloud Solution Architects
- IT Professionals & Consultants
- Machine Learning Engineers
- Business Intelligence Professionals
- Technical Project Managers
Prerequisite
Prerequisites for AI-102: Designing and Implementing a Microsoft Azure AI Solution:
Required:
- Understanding of Microsoft Azure and navigation of the Azure portal
- Proficiency in C# or Python
- Familiarity with JSON and REST programming semantics
Recommended:
- AI-900T00 – Microsoft Azure AI Fundamentals
- AI+ Executive™
- AI+ Prompt Engineer™: Level 1
- AI-050T00 – Develop Generative AI Solutions with Azure OpenAI Service
- AI-3017 – Microsoft AI for Business Leaders
Course Outline
Introduction to Azure AI Services
Preparing for AI solution development on Azure
- Define artificial intelligence concepts
- Recognize key AI-related terminology
- Identify key aspects for AI Engineers
- Explore responsible AI principles
- Learn about Azure Machine Learning capabilities
- Discover features of Azure AI Services
- Understand Azure OpenAI Service functionalities
- Explore Azure AI Search capabilities
Developing and Securing Azure AI Services
Deploy and Utilize Azure AI Services
- Provision Azure AI services resources within an Azure subscription
- Identify necessary endpoints, keys, and locations for service consumption
- Integrate Azure AI services using REST APIs and SDKs
Enhance Security for Azure AI Services
- Implement authentication mechanisms for Azure AI services
- Configure network security for secure access and data protection
Monitoring and Deploying Azure AI Services
Track and Manage Azure AI Services
- Analyze cost metrics for Azure AI services
- Configure alerts and monitor service performance
- Oversee diagnostic logging for troubleshooting
Containerized Deployment of Azure AI Services
- Build reusable containers for AI services
- Deploy and secure AI services within containers
- Access and utilize Azure AI services from a container
Building Computer Vision Solutions with Azure AI Vision
Image Analysis
- Set up an Azure AI Vision resource
- Perform image analysis for insights
- Create smart-cropped thumbnails
Custom Image Classification with Azure AI Vision
- Develop a custom classification model
- Understand image classification techniques
- Explore object detection concepts
- Train an image classifier using Vision Studio
Face Detection, Analysis, and Recognition
- Identify methods for detecting, analyzing, and recognizing faces
- Consider key factors for face analysis
- Detect faces using the Computer Vision service
- Explore the capabilities of the Face service
- Compare, match, and recognize detected faces
- Implement facial recognition solutions
Text Extraction from Images and Documents
- Use OCR to extract text from images
- Implement Image Analysis with SDKs and REST API
- Build applications to read both printed and handwritten text
Video Analysis
- Explore Azure Video Indexer functionalities
- Extract meaningful insights from videos
- Utilize Azure Video Indexer widgets and APIs
Developing Natural Language Processing Solutions with Azure AI Services
Text Analysis with Azure AI Language
- Identify the language of a given text
- Evaluate sentiment within text data
- Extract key phrases, entities, and linked entities
Building Question Answering Solutions with Azure AI Language
- Understand question answering and its differences from language understanding
- Develop, test, publish, and utilize a knowledge base
- Implement multi-turn conversations and active learning strategies
- Create a question-answering bot for natural language interactions
Develop Conversational AI and Speech Solutions with Azure AI Services
Building a Conversational Language Understanding Model
- Set up Azure resources for Azure AI Language
- Define intents, utterances, and entities
- Utilize patterns to distinguish similar utterances
- Implement pre-built entity components
- Train, test, deploy, and refine an Azure AI Language model
Creating a Custom Text Classification Solution
- Identify different classification project types
- Develop a custom text classification model
- Tag data, train, and deploy the model
- Submit classification tasks via an application
Implementing Custom Named Entity Recognition
- Tag entities in extraction projects
- Build entity recognition models
Translating Text with Azure AI Translator Service
- Deploy a Translator resource
- Understand language detection, translation, and transliteration
- Configure translation settings
- Define custom translation models
Developing Speech-Enabled Applications with Azure AI Services
- Configure Azure resources for Azure AI Speech
- Implement speech recognition using the Speech-to-Text API
- Enable speech synthesis using the Text-to-Speech API
- Set audio formats and voice parameters
- Utilize Speech Synthesis Markup Language (SSML)
Translating Speech with Azure AI Speech Service
- Set up Azure resources for speech translation
- Convert spoken language into text translation
- Generate spoken translations
Implement Knowledge Mining with Azure AI Search
Develop an Azure AI Search Solution
- Set up an Azure AI Search solution
- Build a search-enabled application
Create a Custom Skill for Azure AI Search
Develop and integrate a custom skill into an Azure AI Search skillset
Establish a Knowledge Store with Azure AI Search
- Generate a knowledge store from an Azure AI Search pipeline
- View and manage projected data in the knowledge store
Enhance Data with Azure AI Language
- Utilize Azure AI Language to refine Azure AI Search indexes
- Apply custom classes to enrich search indexes
Implement Advanced Search Features in Azure AI Search
- Adjust document ranking using term boosting
- Improve search result relevance with scoring profiles
- Optimize indexing with analyzers and tokenized terms
- Support multiple languages within an index
- Rank search results based on proximity to a reference point
Develop an Azure Machine Learning Custom Skill for Azure AI Search
- Utilize a custom Azure Machine Learning skillset
- Enhance Azure AI Search indexes with Azure Machine Learning
Integrate External Data Sources with Azure AI Search Using Azure Data Factory
- Copy data into an Azure AI Search Index using Azure Data Factory
- Use the Azure AI Search push API to integrate data from external sources
Maintain an Azure AI Search Solution
- Utilize Language Studio for search index enrichment
- Apply custom classes for AI Search index enhancement
Optimize Search Results Using Semantic Ranking in Azure AI Search
- Explain semantic ranking concepts
- Configure and execute semantic ranking on an index
Implement Vector Search and Retrieval in Azure AI Search
- Define vector search concepts and embeddings
- Execute vector search queries via REST API
Develop Solutions with Azure AI Document Intelligence
Design an Azure AI Document Intelligence Solution
- Explain the key components of an Azure AI Document Intelligence system
- Set up and integrate Azure AI Document Intelligence resources in Azure
- Determine when to use prebuilt, custom, or composed models
Utilize Prebuilt Document Intelligence Models
- Identify business cases suited for prebuilt models in Forms Analyzer
- Analyze documents using General Document, Read, and Layout models
- Process forms using financial, ID, and tax-specific prebuilt models
Extract Data from Forms Using Azure Document Intelligence
- Understand how layout services, prebuilt models, and custom models enable automation
- Use SDKs, REST API, and Document Intelligence Studio for document processing
- Build and evaluate custom models for data extraction
Develop a Composed Document Intelligence Model
- Identify scenarios where composed and custom models are beneficial
- Train a custom model for extracting data from diverse document structures
- Create a composed model capable of handling multiple form formats
Create a Custom Document Intelligence Skill for Azure AI Search
- Explain how a custom skill enhances content processing in an Azure AI Search pipeline
- Develop a custom skill integrating Azure Forms Analyzer to extract data from documents
Develop Generative AI Solutions with Azure OpenAI Service
Get Started with Azure OpenAI Service
- Set up an Azure OpenAI Service resource and explore different base models.
- Deploy a model using Azure AI Studio, the console, or REST API and test it in playgrounds.
- Generate responses to prompts and adjust model parameters for better control.
Build Natural Language Solutions with Azure OpenAI Service
- Integrate Azure OpenAI into applications for intelligent text generation.
- Understand different API endpoints and their applications.
- Use the REST API and language-specific SDKs to generate completions.
Apply Prompt Engineering with Azure OpenAI Service
- Learn prompt engineering techniques to optimize model performance.
- Design prompts effectively using clear instructions and structured output requests.
- Use contextual enhancements to improve AI-generated responses.
Generate Code with Azure OpenAI Service
- Use natural language prompts to write and refine code.
- Automate unit test creation and analyze complex code structures.
- Generate code comments and documentation using AI models.
Generate Images with Azure OpenAI Service
- Understand DALL-E capabilities in Azure OpenAI.
- Use the DALL-E playground in Azure AI Studio for image generation.
- Integrate DALL-E via the REST API to generate images in applications.
Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service
- Leverage Azure OpenAI to work with custom datasets.
- Configure Azure OpenAI to process and generate responses based on private data.
- Use the API to generate contextualized responses from specific datasets.
Fundamentals of Responsible Generative AI
- Define a responsible AI framework for generative AI deployment.
- Identify and address potential risks in AI-generated content.
- Measure and mitigate potential biases or harms in AI outputs.
- Implement responsible deployment and operational best practices for AI models.
LAB Module Overview
- Set Up the Lab Environment
- Activate Resource Providers
- Introduction to Azure AI Services
- Configure Security for Azure AI Services
- Track and Manage Azure AI Services
- Deploy and Use an Azure AI Services Container
- Process and Analyze Text Data
- Convert and Translate Text
- Identify and Generate Speech
- Convert and Translate Spoken Language
- Develop a Language Understanding Model with Azure AI Language Service
- Build a Conversational AI Client Application
- Implement a Question Answering System
- Develop a Chatbot Using Bot Framework SDK
- Design a Chatbot with Bot Framework Composer
- Process and Analyze Images Using Azure AI Vision
- Evaluate and Analyze Videos with Video Analyzer
- Train Models for Image Classification with Azure AI Custom Vision
- Detect and Identify Objects in Images Using Custom Vision
- Recognize and Analyze Facial Features
- Extract and Interpret Text from Images
- Retrieve and Process Data from Forms
- Build an Azure AI Search System
- Develop a Custom Skill for Azure AI Search
- Construct a Knowledge Repository with Azure AI Search
About The Certification
About Microsoft Azure AI Engineer Associate:
This certification confirms the proficiency of AI professionals in designing, deploying, and managing AI solutions within the Microsoft Azure environment. AI engineers in this role work closely with cross-functional teams to optimize the use of Azure Cognitive Services and AI-driven applications.
- Role and Responsibilities
- As an Azure AI Engineer, your key responsibilities include:
Establishing AI solution requirements and designing scalable architectures
- Developing, testing, and deploying AI models and applications
- Integrating AI services with existing systems and cloud platforms
- Monitoring AI models for performance, scalability, and reliability
- Enhancing AI workflows and resolving technical challenges
- Ensuring AI solutions comply with security and regulatory standards
As an Azure AI Engineer, you collaborate with solution architects, data scientists, IoT specialists, software developers, and infrastructure teams to:
- Develop secure, end-to-end AI applications
- Integrate AI capabilities into enterprise software solutions
Required Technical Skills
To excel in this role, you should have expertise in:
- Programming Languages: Python, C#
- AI Development Tools: REST APIs, SDKs, and cloud-based AI services
AI Capabilities:
- Image and video processing
- Natural language processing (NLP)
- AI-powered search and knowledge mining
- Generative AI applications
Azure AI Services & Data Management:
- Cognitive Services, Azure Machine Learning, and AI model deployment
- Secure and scalable data storage solutions
- Responsible AI principles and ethical AI practices
Skills Assessed in the Certification
The Microsoft Azure AI Engineer Associate certification evaluates proficiency in:
- Planning and managing AI solutions on Azure (15–20%)
- Implementing AI-driven decision-making systems (10–15%)
- Developing computer vision applications (15–20%)
- Creating natural language processing (NLP) models (30–35%)
- Implementing knowledge mining and document intelligence (10–15%)
- Building generative AI solutions (10–15%)
This certification is designed for AI engineers, data scientists, and software developers seeking to enhance their expertise in Azure AI technologies and advance their careers in AI-driven application development.
About The Examination:
- Passing Score: 700 or higher
- Duration: 120 minutes
Certification Validity and Renewal:
Previously, Microsoft role-based and specialty certifications were valid for two years.
- Since June 2021, role-based and specialty certifications are valid for one year.
- Renewal is free and can be completed online via Microsoft Learn.
- You can renew your certification by passing an online assessment, available six months before expiration.
- Certifications earned before June 2021 remain valid for two years but can still be renewed under the updated process.
Choose Your Preferred Mode
Online Training
- Subject Matter-Authorized Experts
- Official Content
- Approved and Quality Ensured training Material
- 24*7 learner assistance and support
Corporate Training
- ROI-optimization & Group Discounts
- Domain-customization
- 24*7 Learner Assistance and Support
- Instructor-Led Skill Development Program
FAQ’s
What does the Microsoft Certified Azure AI Engineer Associate certification validate?
This certification demonstrates proficiency in designing, deploying, and managing AI-driven solutions within Microsoft Azure. It is intended for professionals utilizing Azure Cognitive Services, machine learning technologies, and AI automation. Earning this certification highlights the ability to develop scalable, secure, and responsible AI applications.
Are there any prerequisites for this certification?
There are no official prerequisites, but candidates should have a solid understanding of AI fundamentals, programming experience in Python or C#, and hands-on familiarity with Azure AI services. Knowledge of REST APIs, SDKs, and machine learning workflows is also advantageous.
Which exam is required to obtain this certification?
Candidates must pass the AI-102: Designing and Implementing an Azure AI Solution exam. This test assesses skills related to AI integration, model deployment, and security best practices, focusing on real-world applications of Azure AI technologies.
How long is the AI-102 exam?
The exam lasts 120 minutes and consists of multiple-choice questions, scenario-based problem-solving, and hands-on assessments. It evaluates both theoretical knowledge and practical expertise in Microsoft Azure AI solutions.
What are the key topics covered in this certification?
The certification focuses on:
- Designing and managing AI-powered solutions on Azure
- Implementing natural language processing (NLP) and computer vision models
- Developing knowledge mining applications and generative AI solutions
- Ensuring AI systems are secure, compliant, and responsible
- Applying Azure AI tools in real-world business scenarios
Who should pursue this certification?
It is ideal for AI engineers, software developers, data scientists, and IT professionals working with AI models, cognitive services, and automation. This certification benefits those integrating AI into business applications or optimizing existing AI workflows.
Can candidates use reference materials during the exam?
No, external resources are not allowed. The exam is proctored and includes interactive exercises and hands-on tasks. Practical experience with Azure AI services and cognitive APIs is recommended for success.
What is the passing score for the AI-102 exam?
A minimum score of 700 out of 1000 is required to pass. Candidates are evaluated based on accuracy, efficiency, and their ability to apply AI concepts effectively.
In which languages is the AI-102 exam available?
The exam is offered in multiple languages, including English, Japanese, Simplified Chinese, Korean, French, German, and Spanish. Candidates should verify language availability in their region before scheduling the test.
Does this course fully prepare candidates for the AI-102 exam?
Yes, the course aligns with exam objectives and includes hands-on labs, real-world projects, and practical exercises. Candidates are encouraged to use practice tests and apply AI concepts in live environments for thorough preparation.
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Reviews
All aspects were covered in the course starting from how to build AI models and up to deploying them on Azure. The best part that I found was the practicality. The use of examples in the classroom made it much easier to grasp various concepts. Vinsys’ learning platform too was very intuitive and the support team was always ready with the help when I needed it. I think that after passing the course, I am much more ready to solve problems with the help of AI solutions in Azure. Definitely would like to recommend this course to anyone who wants to advance in the AI field!
Inderdeep SodhiSoftware Engineer
It was a great decision for my career to take the Designing & Implementing a Microsoft Azure AI Solution course at Vinsys. The course offered a lot of information about AI technologies and what is possible with Azure, and because of the structure it was so much easier to understand. Vinsys ensured that I was fully ready for the exam and I was able to complete it in the first instance. The lecturers were friendly and gave good tips on what is happening in the market. I can even apply what I have learned here to AI projects in my workplace now!
Divya BhandariProject Manager
Taking the Designing & Implementing a Microsoft Azure AI Solution course from Vinsys was just the right thing I needed to advance my career in AI. This was done in a progressive manner, where I was taken through a step-by-step process on how to approach AI problems. The trainers are very good at explaining all the concepts and all the learning aids used where very straightforward. Vinsys also offers great post-training support which was a plus.
Shubham DixitIT Head
In my opinion, Vinsys’ Designing & Implementing a Microsoft Azure AI Solution is one of the most engaging online courses I have taken. The course was really comprehensive, major areas of focus were machine learning, cognitive services and deploying AI models on Azure. The instructors were excellent with profound knowledge of both Azure as well as AI solutions. The content that Vinsys offered for the preparation of the exam was perfect and I did not have any problems passing the exam. I have learned a lot from this course to be able to work on AI projects and therefore looking forward to future projects in AI!
Sajal MalhotraCloud Operation Engineer
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