Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution

The Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution is tailored for professionals working as Azure AI Engineers. These individuals are responsible for designing, developing, deploying, and maintaining AI solutions on the Microsoft Azure platform. The role requires a strong understanding of Azure services and tools, as well as a collaborative mindset to work across multidisciplinary teams.

Key Responsibilities

Azure AI Engineers are involved throughout the entire lifecycle of AI solution development. This includes:

Requirement Gathering & Solution Design
- Collaborating with stakeholders to define business needs and translating them into scalable AI solution architectures.
Development
- Creating intelligent applications using Azure AI services, integrating various APIs, SDKs, and machine learning models.
Deployment & Integration
- Implementing and integrating AI models into existing or new applications with a focus on security, scalability, and performance.
Maintenance & Optimization
- Monitoring deployed solutions to ensure reliability, tuning performance, and updating models or components as needed.
Operational Oversight
- Ensuring continuous monitoring, logging, and diagnostics of AI solutions to maintain optimal functionality and compliance.

Collaboration Across Teams

As an Azure AI Engineer, you work closely with a variety of professionals to deliver end-to-end AI solutions. These include:

Solution Architects – Translating architectural blueprints into technical implementations.
Data Scientists & Engineers – Integrating machine learning models and handling data pipelines.
IoT Specialists & Software Developers – Embedding AI into IoT ecosystems and applications.
Infrastructure Administrators – Managing deployment environments and supporting resource optimization.

Technical Skill Set

To succeed in this role and pass the AI-102 exam, you should be proficient in:

Programming Languages:
- Python
- C#
Azure AI Services & APIs:
Leveraging REST APIs and Azure SDKs to build AI-powered solutions for:
- Image and video analysis
- Natural language processing (NLP)
- Knowledge mining
- Generative AI

Foundational Knowledge Areas

An Azure AI Engineer should have a firm understanding of the following:

Azure AI Ecosystem:
- Familiarity with the various components of the Azure AI portfolio and how they interconnect.
Data Storage Solutions:
- Knowledge of Azure’s data storage offerings and their use cases in AI development.
Responsible AI Practices:
- Applying ethical AI principles, ensuring fairness, transparency, and accountability in AI systems.

Exam Details

The Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution is designed for individuals at an intermediate level aiming to pursue a career as an Azure AI Engineer. This proctored, closed-book assessment has a duration of 100 minutes and may include interactive tasks that simulate real-world AI engineering scenarios. To pass the exam, candidates must achieve a minimum score of 700 out of 1000. The exam is available in multiple languages, including English, Japanese, Simplified Chinese, Traditional Chinese, Korean, German, French, Spanish, Brazilian Portuguese, and Italian, making it accessible to a global audience. Candidates who require accommodations, such as additional time, assistive technologies, or modifications to the exam format—may submit a request for support to ensure an inclusive testing experience.

Course Outline

The exam covers the following topics:

1. Planning and Managing an Azure AI Solution (20-25%)

Select the appropriate Azure AI service

Selecting the proper service for a generative AI solution
Selecting the appropriate service for a computer vision solution
Select the proper service for a natural language processing solution
Select the appropriate service for a speech solution (Microsoft Documentation: What is the Speech service?)
Select the appropriate service for an information extraction solution
Select the proper service for a knowledge mining solution

Plan, create and deploy an Azure AI service

Plan for a solution that meets Responsible AI principles (Microsoft Documentation: What is Responsible AI?)
Creating an Azure AI resource
Choosing the appropriate AI models for your solution
Deploying AI models using the appropriate deployment options
Installing and utilizing the appropriate SDKs and APIs
Determine a default endpoint for a service
Integrating Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline
Planning and implementing a container deployment

Manage, monitor and secure an Azure AI service

Monitoring an Azure AI resource
Managing costs for Azure AI services
Manage and protect account keys
Manage authentication for an Azure AI Service resource

Implement AI solutions responsibly

Implementing content moderation solutions
Configuring responsible AI insights, including content safety
Implementing responsible AI, including content filters and blocklists
Preventing harmful behavior, including prompt shields and harm detection
Designing a responsible AI governance framework

2. Implementing generative AI solutions (15–20%)

Build generative AI solutions with Azure AI Foundry

Plan and prepare for a generative AI solution
Deploy a hub, project, and necessary resources with Azure AI Foundry
Deploy the appropriate generative AI model for your use case
Implement a prompt flow solution
Implement a RAG pattern by grounding a model in your data
Evaluate models and flows
Integrate your project into an application with Azure AI Foundry SDK
Utilize prompt templates in your generative AI solution

Use Azure OpenAI Service to generate content

Provision an Azure OpenAI Service resource
Select and deploy an Azure OpenAI model
Submit prompts to generate code and natural language responses
Use the DALL-E model to generate images
Integrate Azure OpenAI into your own application
Use large multimodal models in Azure OpenAI
Implement an Azure OpenAI Assistant

Optimize and operationalize a generative AI solution

Configure parameters to control generative behavior
Configure model monitoring and diagnostic settings, including performance and resource consumption (Microsoft Documentation: Diagnostic settings in Azure Monitor)
Optimize and manage resources for deployment, including scalability and foundational model updates
Enable tracing and collect feedback
Implement model reflection
Deploy containers for use on local and edge devices (Microsoft Documentation: Give Azure IoT Edge modules access to a device’s local storage)
Implement orchestration of multiple generative AI models
Apply prompt engineering techniques to improve responses
Fine-tune an generative model (Microsoft Documentation: Customize a model with fine-tuning)

3. Implementing an agentic solution (5–10%)

Create custom agents

Understand the role and use cases of an agent
Configure the necessary resources to build an agent
Create an agent with the Azure AI Agent Service
Implement complex agents with Semantic Kernel and Autogen
Implement complex workflows including orchestration for a multi-agent solution, multiple users, and autonomous capabilities
Test, optimize and deploy an agent

4. Implementing computer vision solutions (10–15%)

Analyze images

Select visual features to meet image processing requirements
Detect objects in images and generate image tags
Include image analysis features in an image processing request (Microsoft Documentation: Image Analysis)
Interpret image processing responses
Extract text from images using Azure AI Vision
Convert handwritten text using Azure AI Vision

Implement custom computer vision models

Choose between image classification and object detection models
Label images
Train a custom image model, including image classification and object detection
Evaluate custom vision model metrics
Publish a custom vision model
Consume a custom vision model
Build a custom vision model code first

Analyze videos

Use Azure AI Video Indexer to extract insights from a video or live stream
Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video

5. Implementing Natural Language Processing Solutions (15-20%)

Analyze and translate text

Extract key phrases and entities
Determine sentiment of text
detect the language used in the text (Microsoft Documentation: Detect language with Text Analytics)
Detect personally identifiable information (PII) in text
Translate text and documents by using the Azure AI Translator service

Process and translate speech

Integrate generative AI speaking capabilities in an application
Implement text-to-speech and speech-to-text using Azure AI Speech (Microsoft Documentation: text-to-speech, Custom Voice, speech-to-text)
Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
Implement custom speech solutions with Azure AI Speech
Implement intent and keyword recognition with Azure AI Speech (Microsoft Documentation: Recognize intents with the Speech service and LUIS)
Translate speech-to-text by using the Azure AI Speech service (Microsoft Documentation: speech-to-text)

Implement custom language models

Create intents, entities, and add utterances
Train evaluate, deploy, and test a language understanding model (Microsoft Documentation: deploy a model, Train your conversational language understanding model)
Optimize, backup, and recover language understanding model (Microsoft Documentation: What is Language Understanding (LUIS)?)
Consume a language model from a client application
Create a question answering project (Microsoft Documentation: Create, test, and deploy a custom question answering project)
Add question-and-answer pairs and import sources for question answering
Train, test, and publish a knowledge base (Microsoft Documentation: Test your knowledge base in QnA Maker, Create, train, and publish your QnA Maker knowledge base, Publish the knowledge base)
Create a multi-turn conversation (Microsoft Documentation: create multiple turns of a conversation)
Add alternate phrasing and chit-chat to a knowledge base (Microsoft Documentation: Add additional alternatively-phrased questions, Add alternate questions, Add Chit-chat to a knowledge base)
Export a knowledge base (Microsoft Documentation: Migrate a knowledge base using export-import)
Create a multi-language question answering solution
Implement custom translation, including training, improving, and publishing a custom model

6. Implementing knowledge mining and information extraction solutions (15–20%)

Implement an Azure AI Search solution

Provision an Azure AI Search resource, create an index, and define a skillset
Create data sources and indexers (Microsoft Documentation: Create Data Source)
Implement custom skills and include them in a skillset
create and run an indexer (Microsoft Documentation: Creating indexers in Azure Cognitive Search)
Query an index, including syntax, sorting, filtering, and wildcards (Microsoft Documentation: Simple query syntax in Azure Cognitive Search, Lucene query syntax in Azure Cognitive Search, Querying in Azure Cognitive Search)
Manage knowledge store projections, including file, object, and table projections
Implement semantic and vector store solutions

Implement an Azure AI Document Intelligence solution

Provision a Document Intelligence resource
Use prebuilt models to extract data from documents
Implement a custom document intelligence model
Train, test, and publish a custom document intelligence model
Create a composed document intelligence model

Extract information with Azure AI Content Understanding

Create an OCR pipeline to extract text from images and documents
Summarize, classify, and detect attributes of documents
Extract entities, tables, and images from documents
Process and ingest documents, images, videos, and audio with Azure AI Content Understanding

Microsoft AI-102 Exam FAQs

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Designing and Implementing a Microsoft Azure AI Solution FAQs

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Microsoft AI-102 Exam Study Guide

1. Understand the Exam Objective

To effectively prepare for the AI-102 exam, it is essential to thoroughly understand the exam objectives. These objectives outline the key skills and knowledge areas that candidates are expected to master. The AI-102 exam evaluates your ability to design and implement AI solutions using Microsoft Azure services. It covers areas such as data processing, model deployment, integration of AI solutions into various applications, and ensuring security and scalability. Familiarizing yourself with the exam objectives allows you to focus your preparation on the most relevant topics, ensuring that you are well-equipped to tackle both theoretical questions and practical scenarios that may appear during the exam.

2. Use the Microsoft Official Learning Path

Utilizing the Microsoft Official Learning Path is a crucial step in preparing for the AI-102 exam. This comprehensive resource is designed to guide candidates through the key concepts and skills required for the exam. It covers all essential topics, including designing and implementing AI solutions with Azure services, data processing, model deployment, and ensuring the security and scalability of AI systems. By following the official learning path, you can ensure that your preparation is aligned with the exam objectives, providing structured and detailed insights that will help you effectively prepare for both theoretical and practical aspects of the exam. The modules are:

3. Become Part of Study Groups

Joining study groups can significantly enhance your preparation for the AI-102 exam. Engaging with a community of learners allows you to share insights, clarify doubts, and discuss key concepts, all of which contribute to a deeper understanding of the material. Study groups often provide a collaborative environment where you can explore different approaches to solving problems related to designing and implementing AI solutions with Azure services, data processing, and model deployment. By participating in group discussions and problem-solving activities, you can stay motivated, receive valuable feedback, and reinforce your knowledge, making your exam preparation more effective and comprehensive.

4. Take Microsoft AI-102 Exam Practice Tests

Taking Microsoft AI-102 exam practice tests is an essential part of your preparation strategy. These practice exams simulate the real test environment, allowing you to familiarize yourself with the types of questions and the exam format. By taking these tests, you can assess your understanding of key topics such as designing and implementing AI solutions with Azure services, data processing, and model deployment. Practice tests also help identify areas where you may need to improve, giving you the opportunity to focus your study efforts on weaker sections. Additionally, they help you manage time effectively and reduce exam-day anxiety, ensuring that you are well-prepared for the actual exam.