AI-102T00: Designing and Implementing a Microsoft Azure AI Solution Training & Certification Course
The AI-102T00: Develop AI Solutions in Azure course trains software developers to design and implement AI solutions using Azure Cognitive Services, Azure OpenAI, and Azure AI Search, addressing the critical industry need for professionals who can deploy production-ready AI applications.
Course Overview
Students gain hands-on experience with core Azure AI services including Azure AI Vision, Azure AI Language, Azure AI Speech, Azure AI Search, Azure OpenAI, and Azure AI Document Intelligence within the Azure Portal lab environment. Through practical labs, learners build generative AI applications using Retrieval-Augmented Generation (RAG) patterns, develop AI agents with custom tools and orchestration, implement computer vision solutions for image and face analysis, and create natural language processing workflows for sentiment detection and entity recognition. One key project involves constructing a multimodal AI application that integrates speech-to-text, language understanding, and content safety filters to deliver a production-grade conversational AI solution, ensuring students gain applicable experience with REST APIs and SDKs.
This training prepares candidates for the Microsoft Certified: Azure AI Engineer Associate credential, a globally recognized certification validating expertise in designing and implementing AI solutions on Azure. Certified professionals report average base salaries ranging from $130,000 to $165,000 in the U.S., with senior roles reaching higher compensation. Optiv Solutions enhances preparation with Guaranteed-to-Run classes, official Microsoft courseware, and expert-led instruction that emphasizes real-world implementation and responsible AI practices. Completing the AI-102T00 course positions learners to lead AI integration projects, drive innovation in cloud AI applications, and advance into specialized roles in the rapidly growing field of enterprise artificial intelligence.
Skills You’ll Develop
Who Should Attend
WHO SHOULD ATTEND (TARGET AUDIENCE)
• AI Engineers building and deploying AI solutions on Azure.
• Cloud Developers integrating Azure AI services into applications.
• Application Developers looking to add intelligent capabilities to applications.
• Data Scientists who want to integrate Azure AI capabilities into solutions.
• Machine Learning Engineers working with Azure-based AI applications.
• Solution Architects involved in designing Azure AI solutions.
• Technical Consultants implementing AI solutions for organizations.
• Developers transitioning into Generative AI and Azure AI development.
Pre-requisites
RECOMMENDED KNOWLEDGE BEFORE TAKING THIS COURSE
- ✓ Basic understanding of Microsoft Azure and cloud concepts.
- ✓ Basic programming experience in Python or C#.
- ✓ Familiarity with application development concepts.
- ✓ Basic knowledge of REST APIs and SDKs.
- ✓ Understanding of fundamental AI and machine-learning concepts is beneficial.
- ✓ Familiarity with JSON and HTTP-based APIs is helpful.
- ✓ Basic understanding of authentication and Azure resources is recommended.
Certification Exam Details
Everything you need to know about the certification exam
Exam Details
Upcoming Batch Schedule
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Curriculum & Course Syllabus
Module 1: Plan and Manage an Azure AI Solution
- Introduction to Azure AI
- Azure AI services overview
- Selecting appropriate Azure AI services
- Azure AI resource types
- Provisioning Azure AI resources
- Managing Azure AI resources
- Azure subscriptions and resource groups
- Authentication and authorization
- Azure AI service endpoints
- Azure SDKs
- REST APIs
- Monitoring Azure AI services
- Securing AI resources
- Responsible AI principles
- AI solution architecture
- Cost and scalability considerations
Module 2: Implement Generative AI Solutions
- Introduction to generative AI
- Large language models
- Azure OpenAI
- Microsoft Foundry
- Generative AI models
- Model selection
- Deploying generative AI models
- Working with chat-completion models
- Prompt engineering
- System, user, and assistant messages
- Prompt templates
- Grounding generative AI responses
- Retrieval-Augmented Generation
- Embeddings
- Vector search
- Model evaluation
- Generative AI application integration
- Fine-tuning considerations
- Responsible generative AI
- Content safety
- Monitoring generative AI applications
Module 3: Implement AI Agents
- Introduction to AI agents
- Agent architecture
- Agent instructions
- Agent tools
- Tool calling
- Connecting agents to external data
- Connecting agents to APIs
- Agent orchestration
- Agent workflows
- Grounding agents with enterprise data
- Agent evaluation
- Agent security
- Responsible agent development
- Integrating agents into applications
Module 4: Implement Computer Vision Solutions
- Introduction to computer vision
- Azure AI Vision
- Image analysis
- Image classification
- Object detection
- Image tagging
- Optical Character Recognition
- Face-related capabilities
- Image captioning
- Spatial analysis
- Custom vision concepts
- Computer vision APIs
- SDK-based vision applications
- Processing visual data
- Video analysis
- Azure AI Video Indexer
- Integrating vision capabilities into applications
Module 5: Implement Natural Language Processing Solutions
- Introduction to NLP
- Azure AI Language
- Text analysis
- Key phrase extraction
- Named entity recognition
- Sentiment analysis
- Language detection
- Text classification
- Custom text classification
- Custom named entity recognition
- Conversational language understanding
- Question answering
- Language model integration
- Natural-language application development
- Speech-to-text
- Text-to-speech
- Speech translation
- Conversational AI
- Azure AI Speech
Module 6: Implement Knowledge Mining and Information Extraction
- Introduction to knowledge mining
- Azure AI Search
- Search indexes
- Data sources
- Indexers
- Searchable fields
- Filtering and sorting
- Semantic search
- Vector search
- Hybrid search
- Query processing
- Knowledge stores
- Enrichment pipelines
- AI enrichment
- Document processing
- Azure AI Document Intelligence
- Prebuilt models
- Custom document models
- Extracting structured information
- Forms and invoices
- Tables and key-value pairs
- Information extraction
- Content Understanding
- Integrating extracted information into applications
Student Reviews & Testimonials
Real feedback from certified professionals and corporate teams
Frequently Asked Questions
Is the AI-102 certification exam included in the AI-102T00: Develop AI Solutions in Azure course fee?
The Microsoft AI-102 certification exam is not included in the course tuition and requires separate purchase. The exam fee is $165 USD, subject to regional variations. You must register and pay via Pearson VUE independently of your AI-102T00 training enrollment.
How long is lab access for AI-102T00: Develop AI Solutions in Azure, and what environment is used?
You receive lab access for the course duration plus 30 days post-completion. The training utilizes a live Azure sandbox with real subscriptions, allowing hands-on practice with cognitive services and AI models in a secure, vendor-hosted environment aligned with official Microsoft lab standards.
What is the format, question count, and passing score for the AI-102: Develop AI Solutions in Azure exam?
The AI-102 exam features 40–60 questions, including case studies and drag-and-drop tasks, with a 100-minute limit. You must achieve a 700/1,000 score to pass. Proctored via Pearson VUE, the exam validates your ability to design and implement real-world AI solutions.
How long is the Microsoft Certified: Azure AI Engineer Associate credential valid, and how is it renewed?
This certification remains valid for one year. You must renew it annually by passing a free, unproctored assessment on Microsoft Learn. This assessment covers recent Azure AI updates and is available six months prior to your certification expiration date.
What post-training support does Optiv provide after finishing AI-102T00: Develop AI Solutions in Azure?
Optiv offers 30 days of direct email support from your instructor post-training. You also retain access to session recordings, practice exams, and lab environments. This support helps you resolve implementation challenges and reinforces key concepts for your professional AI development projects.