Search
4323 Partipants
Looking for Corporate TrainingLatest and Advanced Courseware Included
AI-102T00: Designing and Implementing a Microsoft Azure AI Solution Course
The instructor-led online AI-102T00: Designing and Implementing a Microsoft Azure AI Solution training in the UAE provides professionals with practical knowledge to create and deploy and maintain AI solutions on Microsoft Azure platforms. The course teaches essential AI services by exploring comp
AI-102T00 Certification Course
Learn from Certified, Experienced Trainers
100% Exam Preparation Support
Accredited Training Organization
Course Overview
This AI-102T00: Designing and Implementing a Microsoft Azure AI Solution training in the UAE help professionals develop Microsoft Azure AI services for building and managing AI applications. Learners learn a systematic method to create scalable AI solutions through model integration and responsible AI principles for enterprise-level deployment.
The program will provide extensive learning about Azure AI tools through instruction on computer vision together with natural language processing (NLP) and conversational AI and knowledge mining capabilities. This training shows how to unite Azure Cognitive Services with Azure Machine Learning and Azure Bot Services for building intelligent automated applications. AI security and governance stands as a vital part of the course for developing ethical and trustworthy AI solutions among professionals.
The training demonstrates how to integrate deployable AI models so participants can automatically link AI applications with Azure workflows. The Azure Machine Learning pipeline serves as the focus of this training to teach learners how to train models and evaluate them while optimizing their performance for efficient deployment of AI systems.
The training program aims at professionals from three fields: software developers, data scientists and AI engineers to build solution design abilities and AI-102 certification readiness. The practical nature of Azure AI services makes them beneficial for IT professionals who need business understanding of these services.
Participants will achieve complete Microsoft Azure AI proficiency through this training so they can construct data-driven automated solutions that follow industry standards.
Loading...
Course Objectives
- Understand the core components of Microsoft Azure AI, including cognitive services, machine learning, and knowledge mining.
- Gain expertise in designing, developing, and deploying AI models using Azure Machine Learning and Cognitive Services.
- Apply computer vision, natural language processing (NLP), and conversational AI in real-world applications.
- Utilize Azure Bot Services to design and implement intelligent chatbots for automated interactions.
- Improve AI models through training, evaluation, and optimization for enhanced performance.
- Implement responsible AI practices, focusing on bias detection, fairness, transparency, and security compliance.
- Automate AI workflows using Azure Logic Apps, Azure Functions, and AI-powered automation tools.
- Integrate AI models with cloud-based applications and enterprise ecosystems for seamless functionality.
- Develop practical skills through interactive labs, case studies, and real-world AI projects.
- Prepare comprehensively for the AI-102 certification exam, showcasing proficiency in designing and deploying AI solutions on Azure.
Audience
- Machine Learning Engineers
- Business Intelligence Professionals
- Data Scientists
- Cloud Solution Architects
- IT Professionals & Consultants
- AI-102 Certification Candidates
- Technical Project Managers
- DevOps Engineers
- AI Engineers
- Software Developers
Prerequisite
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-900T00 – Microsoft Azure AI Fundamentals
- AI-050T00 – Develop Generative AI Solutions with Azure OpenAI Service
- AI+ Executive™
- AI+ Prompt Engineer™: Level 1
- 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 using Microsoft Azure. AI engineers in this role work with cross-functional teams to leverage Azure Cognitive Services and develop AI-driven applications effectively.
Role and Responsibilities
As an Azure AI Engineer, your key responsibilities include:
- Defining AI solution requirements and designing scalable architectures
- Building, testing, and deploying AI models and applications
- Integrating AI services with enterprise systems and cloud solutions
- Monitoring AI models for performance, scalability, and reliability
- Optimizing AI workflows and resolving technical issues
- Ensuring AI implementations comply with security and regulatory standards
In this role, you collaborate with solution architects, data scientists, IoT specialists, software developers, and infrastructure teams to:
- Integrate AI functionalities into enterprise software solutions
- Build secure, end-to-end AI applications
- 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 designed for AI engineers, data scientists, and software developers aiming to strengthen their skills in Azure AI technologies and advance their careers in AI solution development.
Exam Details:
- 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.
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 expertise in designing, deploying, and managing AI solutions using Microsoft Azure. It is ideal for professionals working with Azure Cognitive Services, machine learning models, and AI-driven automation. Earning this certification validates the ability to build secure, scalable, and responsible AI applications.
Are there any eligibility requirements for this certification?
There are no mandatory prerequisites, but candidates should have a solid understanding of AI principles, programming experience in languages like Python or C#, and hands-on familiarity with Azure AI services. Knowledge of REST APIs, SDKs, and machine learning pipelines is beneficial.
Which exam is required to earn this certification?
Candidates must pass the AI-102: Designing and Implementing an Azure AI Solution exam. This test assesses proficiency in AI integration, model deployment, and AI security best practices, with a focus on real-world applications.
How long is the AI-102 exam?
The exam duration is 120 minutes and consists of multiple-choice questions, scenario-based problem-solving, and hands-on tasks. It evaluates both theoretical knowledge and practical expertise in Azure AI technologies.
What key topics are covered in the Azure AI Engineer certification?
The certification focuses on:
- Designing and managing AI-driven solutions on Azure
- Implementing natural language processing (NLP) and computer vision applications
- Developing knowledge mining solutions and generative AI models
- Deploying secure and responsible AI systems
Who should pursue this certification?
This certification is ideal for AI engineers, software developers, data scientists, and IT professionals working with AI models, cognitive services, and AI-powered automation. It is especially beneficial for those integrating AI into business applications or optimizing existing AI workflows.
Can candidates use reference materials during the exam?
No, the exam is proctored and does not allow external resources. Candidates must complete a mix of theoretical and hands-on assessments. Practical experience with Azure AI services and cognitive APIs is highly recommended.
What is the passing score for the AI-102 exam?
Candidates must score at least 700 out of 1000 to pass. Performance is evaluated based on task accuracy, efficiency, and the 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. Availability may vary by region, so candidates should verify before scheduling.
Does this course fully prepare candidates for the AI-102 exam?
Yes, the course aligns with the AI-102 exam objectives and includes practical exercises, real-world projects, and hands-on labs. For additional preparation, candidates are encouraged to take practice tests and apply AI concepts in real-world scenarios.
Why Vinsys
Seasoned Instructors
Official Vendor Partnerships
Authorized Courseware
3,000+ Courses & 2,000+ Modules
In Synch with Tech-advancements
Customizable Blended Learning Options
Reviews
The AI-102 training provided an exhaustive view of Azure AI solutions. The flexibility in learning options allowed me to balance my professional commitments. It's a must for anyone in the AI domain.
Radhika NageshSoftware Engineer
Vinsys' support helped me transition smoothly into AI development, and I now feel adept at implementing Azure AI solutions.
Yogesh RaiProject Manager
X
Select Language
X
Select Country
X
ENQUIRE NOW