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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 the USA provides professionals with practical skills to design and manage and deploy AI solutions through Microsoft Azure. The course delivers complete education about fundamental AI services
Designing & Implementing a Microsoft Azure AI Solution (AI-102T00) Cer
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Course Overview
The AI-102T00: Designing and Implementing a Microsoft Azure AI Solution training in USA develops competencies for professionals to develop and maintain AI-powered applications through Microsoft Azure AI services. The course provides learners a structured approach that allows them to develop scalable AI systems which integrate machine learning elements with enterprise deployment standards of responsible AI.
This program trains participants about all Azure AI tools that span from computer vision to natural language processing and conversational AI alongside knowledge mining functionalities. The participants will learn how to create intelligent applications by combining Azure Cognitive Services with Azure Machine Learning and Azure Bot Services. The program delivers two essential components by teaching security practices for AI along with governance models to enable professionals in developing trustworthy ethical AI solutions.
The training provides students with deployable AI model integration techniques which let them combine cloud applications through automated workflow deployment on Azure AI platforms. Learners must understand the Azure Machine Learning pipeline for model training along with evaluation and optimization and deployment processes to make fundamental learning progress in this program.
The program exists to support AI engineers and data scientists and software developers who aim to develop their AI design capabilities while pursuing AI-102 certification. IT professionals who desire business insights into Azure AI services receive important information through this training.
The program concludes by giving participants comprehensive Microsoft Azure AI expertise to develop intelligent automated data-driven solutions.
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Course Objectives
- Explore the fundamental elements of Microsoft Azure AI, including cognitive services, machine learning, and knowledge discovery.
- Develop expertise in designing, building, and deploying AI models using Azure Machine Learning and Cognitive Services.
- Apply computer vision, natural language processing (NLP), and conversational AI in practical scenarios.
- Leverage Azure Bot Services to create and integrate intelligent chatbots for automated communication.
- Enhance AI models through training, evaluation, and fine-tuning techniques for optimal performance.
- Implement responsible AI principles, addressing bias detection, fairness, transparency, and security compliance.
- Automate AI processes using Azure Logic Apps, Azure Functions, and AI-driven automation tools.
- Seamlessly integrate AI models with existing cloud-based applications and enterprise systems.
- Gain hands-on experience through interactive labs, case studies, and real-world AI solution development.
- Get fully prepared for the AI-102 certification exam, demonstrating proficiency in designing and deploying Azure AI solutions.
Audience
- Machine Learning Engineers
- Business Intelligence Professionals
- Technical Project Managers
- DevOps Engineers
- AI Engineers
- Software Developers
- Data Scientists
- Cloud Solution Architects
- IT Professionals & Consultants
- AI-102 Certification Candidates
Eligibility Criteria
Prerequisites for AI-102: Designing and Implementing a Microsoft Azure AI Solution:
Required:
- Proficiency in C# or Python
- Understanding of Microsoft Azure and navigation of the Azure portal
- Familiarity with JSON and REST programming semantics
Recommended:
- AI+ Executive™
- AI+ Prompt Engineer™: Level 1
- AI-900T00 – Microsoft Azure AI Fundamentals
- 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 validates the expertise of AI professionals in developing, deploying, and managing AI-driven solutions within the Microsoft Azure ecosystem. AI engineers in this role collaborate with cross-functional teams to maximize the capabilities of Azure Cognitive Services and AI-powered applications.
Role and Responsibilities
As an Azure AI Engineer, your key responsibilities include:
- Defining AI solution requirements and designing robust architectures
- Developing, testing, and deploying AI models and applications
- Integrating AI services with existing systems and cloud-based solutions
- Monitoring AI models for performance, scalability, and efficiency
- Optimizing AI workflows and troubleshooting issues
- Ensuring AI implementations adhere to security and compliance standards
In this role, you collaborate with solution architects, data scientists, IoT specialists, software developers, and infrastructure teams to:
- Build secure, end-to-end AI applications
- Integrate AI functionalities into enterprise software solutions
- Technical Skills Required
To succeed as an Azure AI Engineer, you should have proficiency 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-driven 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 knowledge in the following areas:
- 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 ideal for AI engineers, data scientists, and software developers looking to enhance their expertise in Azure AI technologies and advance their careers in AI-driven application development.
About The Exam :
- Passing Score: 700 or higher
- Duration: 120 minutes
Certification Validity and Renewal:
Previously, Microsoft role-based and specialty certifications were valid for two years.
- As of June 2021, Microsoft certifications are valid for one year.
- Renewals can be completed online for free via Microsoft Learn.
- You can renew your certification by passing an online assessment, available six months before expiration.
- Certifications obtained before June 2021 remain valid for two years but can still be renewed under the updated process.
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- Instructor-Led Skill Development Program
FAQ’s
What does the Microsoft Certified Azure AI Engineer Associate certification validate?
This certification confirms expertise in developing, deploying, and managing AI-based solutions using Microsoft Azure. It is designed for professionals working with Azure Cognitive Services, machine learning technologies, and AI-driven automation. Achieving this certification showcases the ability to create scalable, secure, and ethical AI applications.
Are there any eligibility criteria for this certification?
There are no formal prerequisites, but candidates should have a strong foundation in AI principles, proficiency in programming languages like Python or C#, and hands-on experience with Azure AI services. Knowledge of REST APIs, SDKs, and machine learning pipelines is also beneficial.
Which exam must be cleared to earn this certification?
Candidates need to pass the AI-102: Designing and Implementing an Azure AI Solution exam. This assessment evaluates skills related to AI integration, deploying models, and implementing AI security best practices. The exam focuses on real-world scenarios to ensure candidates can effectively manage AI-powered applications.
What is the duration of the AI-102 exam?
The test lasts 120 minutes and includes multiple-choice questions, scenario-based problem-solving, and practical assessments. It measures both theoretical understanding and practical expertise in Microsoft Azure AI technologies.
What key areas are covered in the Azure AI Engineer certification?
The certification curriculum includes:
- Designing and managing AI-driven solutions on Azure
- Implementing natural language processing (NLP) and computer vision applications
- Creating knowledge mining solutions and generative AI models
- Deploying secure and responsible AI systems
- The course provides comprehensive insights into Azure AI tools and their real-world applications.
Who should consider obtaining this certification?
This certification is best suited for AI engineers, software developers, data scientists, and IT specialists working with AI models, cognitive services, and automated systems. It is especially useful for professionals incorporating AI into business applications or enhancing existing AI workflows.
Can candidates refer to study materials during the exam?
No, the exam is supervised and does not permit external references. Candidates must complete a combination of interactive exercises and hands-on tasks. Practical experience with Azure AI services and cognitive APIs is recommended for better performance.
What is the minimum passing score for the AI-102 exam?
A minimum score of 700 out of 1000 is required to pass. Performance is evaluated based on task accuracy, efficiency, and practical AI implementation.
In which languages can candidates take the AI-102 exam?
The exam is available in several languages, including English, Japanese, Simplified Chinese, Korean, French, German, and Spanish. Candidates should check for language availability in their region before scheduling the test.
Does this course adequately prepare candidates for the AI-102 exam?
Yes, the course aligns with the exam objectives and includes practical exercises, real-world projects, and hands-on labs. To further enhance preparation, candidates are encouraged to utilize practice tests and apply AI concepts in real-world scenarios.
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Reviews
The broad coverage of AI solutions in the AI-102 program has helped our team's proficiency. The hands-on exercises and industry-aligned modules make it a top choice for corporate training.
Rakesh MeheraSoftware Engineer
As a Senior Software Developer, I appreciate the broad nature of the AI-102 course. Vinsys' training empowered our team with in-depth knowledge and practical skills. It has enabled us to leverage Azure AI services more effectively in our projects.
Rima GulatiSoftware Engineer
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