Microsoft Practice Test AI-102: Designing and Implementing an Azure AI Solution

The Microsoft AI-102 practice test offers a comprehensive training experience designed to help professionals master the implementation and management of artificial intelligence solutions within the Microsoft Azure ecosystem. This robust preparation tool includes updated, in-depth content focusing on integrating Azure OpenAI services, constructing cutting-edge generative AI applications, and applying vital responsible AI principles. By engaging with this practice test, you will develop practical, hands-on expertise using the latest Azure SDKs and REST APIs to build sophisticated solutions that span natural language processing, advanced image and video analysis, knowledge mining, and real-time AI processing. Designed to meet the evolving demands of modern AI engineering, the test features both a certification mode to accurately assess your exam readiness and pinpoint weak areas, and a practice mode that allows you to target and strengthen specific skills.

Note: This is merely a practice test to prepare for the professional certification exam, and no certificate is issued by the center for passing it.

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Why should I use the AI-102 Practice Test to prepare for the official exam?

Preparing for the AI-102 exam requires a deep understanding of complex Azure AI services, and this practice test is the most effective way to validate your skills before test day. It accurately mirrors the format, difficulty, and objective domains of the official certification, ensuring there are no surprises when you sit for the actual exam. By utilizing the dual modes of testing, you can simulate the high-pressure environment of the real test while also taking the time to review detailed explanations for every question. This targeted approach not only boosts your confidence but also bridges any knowledge gaps, maximizing your chances of passing the certification on your first attempt and proving your proficiency as an Azure AI Engineer.

The AI-102 practice test contains 134 questions and covers the following objectives:

Plan and manage an Azure AI solution – 27 questions

Select the appropriate Azure AI Foundry services

• Select the appropriate service for a generative AI solution
• Select the appropriate service for a computer vision solution
• Select the appropriate service for a natural language processing solution
• Select the appropriate service for a speech solution
• Select the appropriate service for an information extraction solution
• Select the appropriate service for a knowledge mining solution

Plan, create and deploy an Azure AI Foundry service

• Plan for a solution that meets Responsible AI principles
• Create an Azure AI resource
• Choose the appropriate AI models for your solution
• Deploy AI models using the appropriate deployment options
• Install and utilize the appropriate SDKs and APIs
• Determine a default endpoint for a service
• Integrate Azure AI Foundry Services into a continuous integration and continuous delivery (CI/CD) pipeline
• Plan and implement a container deployment

Manage, monitor, and secure an Azure AI Foundry Service

• Monitor an Azure AI resource
• Manage costs for Azure AI Foundry Services
• Manage and protect account keys
• Manage authentication for an Azure AI Foundry Service resource

Implement AI solutions responsibly

• Implement content moderation solutions
• Configure responsible AI insights, including content safety
• Implement responsible AI, including content filters and blocklists
• Prevent harmful behavior, including prompt shields and harm detection
• Design a responsible AI governance framework

Implement generative AI solutions – 27 questions

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 in Foundry Models to generate content

• Provision an Azure OpenAI in Foundry Models 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
• 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
• Implement orchestration of multiple generative AI models
• Apply prompt engineering techniques to improve responses
• Fine-tune an generative model

Implement an agentic solution 8 – questions

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 Foundry 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

Implement computer vision solutions 23 – questions

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
• Interpret image processing responses
• Extract text from images using Azure AI Vision
• Convert handwritten text using Azure AI Vision

Implement custom 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

Implement natural language processing solutions 33 – questions

Analyze and translate text

• Extract key phrases and entities
• Determine sentiment of text
• Detect the language used in text
• 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
• 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
• Translate speech-to-speech and speech-to-text by using the Azure AI Speech service

Implement custom language models

• Create intents, entities, and add utterances
• Train, evaluate, deploy, and test a language understanding model
• Optimize, backup, and recover language understanding model
• Consume a language model from a client application
• Create a custom question answering project
• Add question-and-answer pairs and import sources for question answering
• Train, test, and publish a knowledge base
• Create a multi-turn conversation
• Add alternate phrasing and chit-chat to a knowledge base
• Export a knowledge base
• Create a multi-language question answering solution
• Implement custom translation, including training, improving, and publishing a custom model

Implement knowledge mining and information extraction solutions 16 – questions

Implement an Azure AI Search solution

• Provision an Azure AI Search resource, create an index, and define a skillset
• Create data sources and indexers
• Implement custom skills and include them in a skillset
• Create and run an indexer
• Query an index, including syntax, sorting, filtering, and wildcards
• 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

Notes:

• The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
• Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.

Don’t leave your certification success to chance. Equipping yourself with the AI-102 practice test is the smartest investment you can make toward achieving your Azure AI Engineer credentials. Take advantage of the comprehensive coverage, realistic exam simulations, and detailed feedback to transform your preparation journey. Secure your access today, master the intricacies of Azure AI solutions, and take a definitive step forward in accelerating your tech career!