Microsoft Azure AI-102 Exam Prep: Design & Deploy AI Solutions on Azure

Master AI-102 Exam Skills to Build, Secure, and Deploy Enterprise AI Solutions Using Microsoft Azure Services

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Microsoft Azure AI-102 Exam Prep: Design & Deploy AI Solutions on Azure

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Course Overview:

The "Microsoft Azure AI-102 Exam Prep: Design & Deploy AI Solutions on Azure Training Course" is an advanced, hands-on program crafted to equip AI Engineers and developers with the skills and confidence to design, implement, and manage Azure-based AI solutions. This course is aligned with the AI-102 Certification Training and prepares participants to pass the Microsoft Certified AI-102 Exam. Throughout the program, learners will master Azure Cognitive Services Training, Natural Language Processing in Azure, Azure Bot Service Development, Azure Computer Vision API Training, Azure Knowledge Mining Course, and how to Deploy AI Solutions with Azure SDK. This training offers in-depth, real-world scenarios using C# or Python, REST APIs for Azure AI, and tools like Custom Vision, Azure Document Intelligence, and Azure Cognitive Search Implementation. This course is ideal for software engineers, data specialists, and technical leaders aiming to elevate their Azure AI expertise and obtain Microsoft Azure AI Engineer certification.

Target Audience:

AI Engineers
Software Developers
Data Scientists
Machine Learning Engineers
Solution Architects
IT Consultants

Targeted Organizational Departments:

IT & Software Development Teams
Data & Analytics Departments
AI & Machine Learning Units
Cloud Architecture and DevOps Teams
Innovation and R&D Divisions

Targeted Industries:

Information Technology
Finance & Banking
Healthcare & Life Sciences
Retail & E-Commerce
Government & Smart Cities
Education & Research Institutions

Course Offerings:

By the end of this course, participants will be able to:

Build secure AI-infused applications using Azure Cognitive Services and REST APIs for Azure AI.
Implement Azure AI Services including NLP, vision, bots, and knowledge mining.
Design and deploy custom solutions using Azure SDKs and containers.
Analyze images, video, text, and speech using Microsoft Azure AI services.
Pass the Microsoft Certified AI-102 Exam confidently.
Utilize tools like Azure Document Intelligence Models, Custom Vision in Azure, Azure Bot Service, and Azure Cognitive Search.

Training Methodology:

This course employs a blended training methodology including real-time case studies, interactive lab exercises, expert-led sessions, group discussions, and mock AI-102 practice tests. Participants will gain hands-on experience using Azure SDK, REST-based APIs, and C# or Python scripting to implement scalable AI solutions. The training emphasizes real-world implementation through AI-102-aligned assessments. Interactive sessions with scenario-based challenges, feedback loops, and demo labs covering Azure Machine Learning with AI and Speech-to-Text and Text-to-Speech with Azure will ensure knowledge retention and practical readiness.

Course Toolbox:

Study guide (digital handout)
Sample projects using Azure Bot Framework & Azure Document Intelligence
REST API reference sheets
AI-102 Practice Tests and mock questions
Access to Microsoft Learn labs (via links)
Case studies, checklists, and Azure SDK templates

Note: Tool licenses or platforms are not provided. Instead, real-world tool use is demonstrated using publicly available Microsoft Azure resources.

Course Agenda:

Day 1: Planning, Security & Responsible AI

Topic 1: Understand AI-102 Exam Scope & Azure AI Engineer Role
Topic 2: Select the Appropriate Azure AI Services for Vision, Language, and Speech
Topic 3: Plan for Responsible AI and Ethical Implementation
Topic 4: Configure Security for Azure AI Services and Manage Authentication
Topic 5: Create and Manage Azure AI Resources using Portal and SDK
Topic 6: Plan for Container Deployments and CI/CD Integration of AI Services
Reflection & Review: Reviewing exam weightings, key security practices, and planning checklists

Day 2: Computer Vision & Image Intelligence

Topic 1: Analyze Images using Computer Vision – Tags, Categories, Objects
Topic 2: Extract Text from Images and Handwriting using OCR
Topic 3: Implement Custom Vision for Image Classification and Object Detection
Topic 4: Train and Evaluate Custom Models with Compact or Cloud Hosting Options
Topic 5: Export Models for Edge Deployment and Container Use
Topic 6: Process Videos using Azure Video Indexer – Extracting Insights & Transcripts
Reflection & Review: Compare built-in vs custom vision tools, prepare for scenario-based vision questions

Day 3: Natural Language & Speech Intelligence

Topic 1: Analyze Text – Key Phrases, Entities, Sentiment, and PII Detection
Topic 2: Implement Text-to-Speech and Customize Voice Output with SSML
Topic 3: Implement Speech-to-Text and Enhance Accuracy with Custom Models
Topic 4: Translate Text and Speech in Real Time across Multiple Languages
Topic 5: Build and Optimize Language Understanding (LUIS) Models
Topic 6: Create Question Answering Systems with Multi-Turn Dialogue and Metadata
Reflection & Review: Exam tips for text analytics, LUIS structure, and voice service integration

Day 4: Bots, Orchestration & Multi-Channel Conversational AI

Topic 1: Design Conversational Flows and Dialog Logic
Topic 2: Build Bots Using Azure Bot Framework SDK and Composer
Topic 3: Implement Multi-Language Support and Adaptive Cards in Bots
Topic 4: Integrate Language, QnA, and Speech Services into a Unified Bot
Topic 5: Test Bots in Emulator and Live Channels like Teams or Web Chat
Topic 6: Publish, Deploy, and Monitor Bots in Production
Reflection & Review: Discuss conversational architecture, troubleshoot bot failures, review adaptive flows

Day 5: Cognitive Search, Knowledge Mining & Exam Preparation

Topic 1: Provision and Configure Azure Cognitive Search
Topic 2: Define Indexes, Data Sources, and Build Indexers
Topic 3: Apply AI Enrichment with Built-In and Custom Skills
Topic 4: Deploy AI Models using SDK, REST APIs, and Containers
Topic 5: Monitor Costs, Set Alerts, and Enable Diagnostic Logging for AI Services
Topic 6: Take AI-102 Practice Exam and Review Key Exam Domains
Reflection & Review: Final Q&A, Confidence Check, Mock Scenarios, Exam Strategy Planning

FAQ:

What specific qualifications or prerequisites are needed for participants before enrolling in the course?

Participants should have working knowledge of Microsoft Azure, experience with programming in C# or Python, and familiarity with REST APIs.

How long is each day's session, and is there a total number of hours required for the entire course?

Each day's session is generally structured to last around 4-5 hours, with breaks and interactive activities included. The total course duration spans five days, approximately 20-25 hours of instruction.

What is the difference between Azure Cognitive Search and Azure Cognitive Services?

Azure Cognitive Services are pre-built APIs for vision, speech, and language, while Azure Cognitive Search combines indexing with AI enrichment to enable intelligent search experiences. Both are essential for AI-102 Certification Training.

How This Course is Different from Other Microsoft Azure AI-102 Exam Prep Courses:

Unlike many AI-102 Exam Preparation programs that focus only on theoretical learning, this course provides a high-impact, project-based training approach. It incorporates hands-on labs using REST APIs for Azure AI, Custom Vision in Azure, Azure Bot Framework, and Azure Document Intelligence Models. Participants will not only understand how to pass the Microsoft Certified AI-102 Exam but will be able to immediately implement AI-powered solutions in their work environments. This course also integrates insights from Azure Machine Learning with AI and emphasizes Responsible AI principles and real-world deployment scenarios, making it highly suitable for developers, engineers, and architects looking to apply their learning in enterprise-scale solutions.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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