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Designing & Implementing a Microsoft Azure AI Solution (AI-102T00) Certification
AI-102 Certification Training
Master AI Solution Development through Microsoft Azure training in Saudi Arabia. The AI-102T00: Designing and Implementing a Microsoft Azure AI Solution training in Saudi Arabia provides students with extensive knowledge and practical skills. The program delivers online instructor guidance to exp
AI-102T00
Microsoft Official Curriculum
Microsoft Certified Trainers
Certificate of Attendance From Microsoft
AI-102T00: Designing & Implementing a Microsoft Azure AI Solution Course Overview
The AI-102T00 training in Saudi Arabia provides professionals the essential skills needed to build AI applications through the deployment and management of Microsoft Azure AI services. The training system provides a systematic learning structure to help participants create AI solutions that scale and implement machine learning models with cognitive services and responsible AI practices that meet enterprise deployment requirements.
Students learn all Azure AI tools from computer vision to natural language processing (NLP) and conversational AI and knowledge mining through the program. The program allows participants to construct intelligent applications through practical experience with Azure Cognitive Services and Azure Machine Learning and Azure Bot Services integration. AI security best practices and governance models take center stage in the training because professionals need to develop ethical and trustworthy AI solutions.
The training includes hands-on practical labs together with real-world case studies which show learners how to deploy AI models and optimize them and integrate workflows automatically in the Azure platform. Students learn Azure Machine Learning pipeline management through the curriculum which provides expertise in model training along with evaluation and deployment methods for practical application development.
The training suits professionals from AI engineering through data science and software development and IT roles who wish to improve their AI solution development skills and prepare for the AI-102 certification exam. Business professionals seeking knowledge about Azure AI services will achieve valuable understanding through comprehensive understanding of AI-powered business applications.
After completing the program participants will master Microsoft Azure AI technologies and learn to develop automated AI solutions that leverage enterprise data for applications.
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Course Objectives
- Understand the core components of Microsoft Azure AI, including machine learning, cognitive services, and knowledge mining.
- Gain proficiency in developing, deploying, and managing AI models using Azure Machine Learning and Cognitive Services.
- Apply computer vision, NLP, and conversational AI to solve real-world business challenges.
- Utilize Azure Bot Services to design and integrate intelligent chatbots for automated customer interactions.
- Improve AI model performance through training, evaluation, and optimization techniques.
- Implement ethical AI principles, focusing on bias detection, fairness, transparency, and security compliance.
- Automate AI workflows using Azure Logic Apps, Azure Functions, and AI-driven automation.
- Seamlessly connect AI models with cloud applications and enterprise systems for scalable solutions.
- Develop practical skills through hands-on labs, case studies, and real-world AI implementation projects.
- Prepare thoroughly for the AI-102 certification exam, demonstrating expertise in designing and deploying Azure AI solutions.
Audience
- Data Scientists
- Cloud Solution Architects
- IT Professionals & Consultants
- AI-102 Certification Candidates
- Machine Learning Engineers
- Business Intelligence Professionals
- Technical Project Managers
- DevOps Engineers
- AI Engineers
- Software Developers
Prerequisite
Prerequisites for AI-102: Designing and Implementing a Microsoft Azure AI Solution:
Required:
- Strong knowledge of C# or Python programming languages.
- Experience with Microsoft Azure and the ability to navigate the Azure portal.
- Understanding of JSON format and REST API principles for application development.
Recommended:
- AI-050T00 – Develop Generative AI Solutions with Azure OpenAI Service
- AI-3017 – Microsoft AI for Business Leaders
- AI+ Executive™
- AI+ Prompt Engineer™: Level 1
- AI-900T00 – Microsoft Azure AI Fundamentals
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 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 demonstrates the proficiency of AI professionals in designing, implementing, and overseeing AI-based solutions within the Microsoft Azure environment. AI engineers in this role work closely with diverse teams to leverage Azure Cognitive Services and develop intelligent, scalable AI applications.
Role and Responsibilities
As an Azure AI Engineer, your key responsibilities include:
- Identifying AI solution requirements and creating resilient architectures
- Building, evaluating, and deploying AI models and applications
- Connecting AI services with existing infrastructure and cloud-based platforms
- Tracking AI model performance, scalability, and operational efficiency
- Enhancing AI workflows and resolving technical challenges
- Ensuring AI solutions comply with security protocols and industry regulations
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 well-suited for AI engineers, data scientists, and software developers aiming to strengthen their knowledge of Azure AI technologies and progress in AI-powered application 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 remain valid for one year.
- Renewals are free and can be completed online through Microsoft Learn.
Candidates can renew their certification by passing an online assessment, which becomes available six months before the certification expires.
Certifications earned before June 2021 are valid for two years but can still be renewed following 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 credential signify?
This certification demonstrates proficiency in designing, implementing, and managing AI solutions on Microsoft Azure. It is intended for professionals working with Azure Cognitive Services, AI automation, and machine learning frameworks. Earning this credential highlights the ability to develop secure, scalable, and ethical AI applications.
Are there any prerequisites for this certification?
There are no mandatory requirements, but candidates should have a solid understanding of AI concepts, be skilled in programming languages such as Python or C#, and have hands-on experience with Azure AI services. Familiarity with 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 evaluates expertise in AI model deployment, security implementation, and integration of AI services into applications. It focuses on real-world use cases to assess practical skills.
How long is the AI-102 exam?
The exam lasts for 120 minutes and consists of multiple-choice questions, scenario-based problem-solving tasks, and hands-on assessments. It evaluates both theoretical knowledge and practical abilities in Azure AI technologies.
What topics are included in the Azure AI Engineer certification?
The certification covers:
- Designing and implementing AI-powered solutions on Azure
- Developing applications using natural language processing (NLP) and computer vision
- Creating knowledge mining and generative AI models
- Ensuring AI security, governance, and compliance
The program provides an in-depth understanding of Azure AI tools and their real-world applications.
Who should pursue this certification?
AI engineers, software developers, data scientists, and IT professionals working with AI-driven applications, automation, and cognitive services will benefit from this certification. It is particularly valuable for those integrating AI into business processes or optimizing AI models.
Are study materials allowed during the exam?
No, external resources are not permitted. The exam is monitored and requires candidates to complete interactive exercises and practical assignments. Prior experience with Azure AI tools and cognitive APIs is recommended for better performance.
What is the passing score for the AI-102 exam?
A minimum score of 700 out of 1000 is required to pass. Scores are based on task completion, problem-solving accuracy, and overall AI implementation skills.
In which languages is the AI-102 exam available?
The exam can be taken in multiple languages, including English, Japanese, Korean, French, German, Spanish, and Simplified Chinese. Candidates should verify the available language options before scheduling their test.
Does this course provide sufficient preparation for the AI-102 exam?
Yes, the course is aligned with the exam objectives and includes hands-on projects, practical exercises, and interactive labs. To enhance preparation, candidates are advised to use practice tests and apply AI concepts in real-world scenarios.
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Vinsys' DP-100T01 course reshaped my data science career. The hands-on experience with Microsoft Azure, Python, and machine learning was immediately applicable to my projects. Engaging online sessions and 24/7 support ensured a thorough preparation for the DP-100 exam. Highly recommended for serious data science enthusiasts.
Dilieep RathodData Scientist
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