AI-102 Study Cram - Azure AI Engineer Associate Certification

This is an AI-102 Study Cram which serves as a guideline for the Designing and Implementing a Microsoft Azure AI Solution exam, leading to the Azure AI Engineer Associate certification. The study cram covers a range of topics such as introduction to AI, Azure ML, Azure Cognitive Services, network protection, and Responsible AI. It further delves into details about deploying to Azure, REST API and SDK, Pricing, and monitoring

• Introduction The session is opened, setting the context for the entire presentation. Key points include the importance of AI and machine learning in today's technological landscape, and an outline of what will be covered.
• Recommended learning Here, learning resources and pathways will be suggested to enable a deeper understanding of AI and related Microsoft technologies.
• Responsible AI This section introduces the principles of Responsible AI, discussing how it ensures fairness, privacy, transparency, and security in AI applications.
• What is AI This section defines AI, its categories, use-cases, and an overview of its history and advancements.
• Azure ML An introduction to Azure Machine Learning, a cloud-based environment provided by Microsoft.
• Azure Cognitive Services Azure Cognitive Services, which provides pre-built AI services like language understanding, vision, and decision-making, will be introduced and explained.
• Deploying to Azure Here, the process of deploying models and applications to Azure is described, including any considerations or best practices to be aware of.
• Network protection This part discusses Azure's capabilities for network protection, ensuring the safety and security of applications deployed on its platform.
• REST API and SDK In this section, the usage of REST APIs and SDKs in context of Azure is explained. How they can be utilized to interact with Azure services is covered.
• Pricing and monitoring The different pricing tiers of Azure, how to choose between them, and how to monitor usage to control costs are discussed.

• Deploying to containers This part explains how to containerize applications for deployment on Azure, covering technologies like Docker and Kubernetes.
• Visual Perception services Here, Azure's capabilities for visual perception (like image recognition and analysis) using AI are covered.
• Language Understanding This section dives into the language understanding capabilities provided by Azure, including things like sentiment analysis and key phrase extraction.
• Question answering and text analysis Azure's capabilities for answering questions and analyzing text using AI (such as Text Analytics API or QnA Maker) are discussed.
• Translation Here, the translation services provided by Azure are covered, explaining how they use AI for accurate and context-aware translations.
• Speech services This section discusses Azure's speech services, which include speech-to-text, text-to-speech, and speech translation.
• Speech translation The capabilities of Azure's AI for translating spoken language in real-time are covered here.
• Decision making This part discusses how Azure's AI can be used to make decisions, like recommendations or anomaly detection.
• Forms recognizer and other pre-built The capabilities of Azure's pre-built AI models, like the Form Recognizer for extracting information from documents, are covered.
• Bot service Azure's Bot Service, which allows developers to build, test, deploy, and manage intelligent bots, is discussed.
• Cognitive service Azure's cognitive services, providing pre-built AI models in vision, speech, language, and decision-making, are further discussed.
• Azure OpenAI services The session will cover Azure's integration with OpenAI, discussing services like GPT-3.

Additionally, it discusses topics like Visual Perception services, Language Understanding, Text analysis, Translation, Speech services, Speech translation, Decision making, Forms recognizer, Bot service, Cognitive service, and Azure OpenAI services.

More About The Main Topic

The AI-102 Study Cram is an effective preparation resource for the Designing and Implementing a Microsoft Azure AI Solution exam. The material is compact, precise, and carefully structured covering all necessary topics. From basics to more-specifics of Azure AI, each chapter has been planned meticulously to provide a complete understanding of the subject required for the certification. It's a very helpful tool to prepare for the Azure AI Engineer Associate certification.

Learn about AI-102 Study Cram - Azure AI Engineer Associate Certification

 

The AI-102 Designing and Implementing a Microsoft Azure AI Solution exam covers a wide range of topics related to AI technology, and prepares candidates for the Azure AI Engineer Associate certification. This study cram covers the topics of Responsible AI, Azure ML, Azure Cognitive Services, Deploying to Azure, Network protection, REST API and SDK, Pricing and monitoring, Deploying to containers, Visual Perception services, Language Understanding, Question answering and text analysis, Translation, Speech services, Speech translation, Decision making, Forms recognizer and other pre-built, Bot service, Cognitive service, Azure OpenAI services, and Summary.

The study cram will cover the basics of AI technology, Responsible AI, and how to use Azure ML, Cognitive Services, and OpenAI for AI solutions. It will also cover topics such as deploying AI solutions to Azure, network protection, REST API and SDK, pricing and monitoring, deploying to containers, visual perception services, language understanding, question answering and text analysis, translation, speech services, speech translation, decision making, forms recognizer and other pre-built, bot service, cognitive service, and Azure OpenAI services.

Candidates will also learn about the different types of AI services available on Azure, such as ML, Cognitive Services, and OpenAI, as well as how to deploy AI solutions on Azure. Additionally, the study cram will cover topics such as network protection, pricing and monitoring, REST API and SDK, deploying to containers, visual perception services, language understanding, question answering and text analysis, translation, speech services, speech translation, decision making, forms recognizer and other pre-built, bot service, cognitive service, and Azure OpenAI services.

Overall, this study cram will provide a comprehensive overview of the topics related to Azure AI Engineer Associate certification and will prepare candidates for taking the AI-102 Designing and Implementing a Microsoft Azure AI Solution exam.

 

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Keywords

AI-102 Exam, AI Engineer Associate Certification, Responsible AI, Azure ML, Azure Cognitive Services, Network Protection, REST API, SDK, Pricing and Monitoring, Containers, Visual Perception Services, Language Understanding, Question Answering, Text Analysis, Translation, Speech Services, Speech Translation, Decision Making, Forms Recognizer, Bot Service, Cognitive Service, Azure OpenAI Services.