Exam Name: Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版)
Certification Provider: Microsoft
Corresponding Certification: Azure AI Engineer Associate
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Microsoft AI-102 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Plan and Manage an Azure Cognitive Services Solution (15-20%) | |
| Select the appropriate Cognitive Services resource | - select the appropriate cognitive service for a vision solution - select the appropriate cognitive service for a language analysis solution - select the appropriate cognitive Service for a decision support solution - select the appropriate cognitive service for a speech solution |
| Plan and configure security for a Cognitive Services solution | - manage Cognitive Services account keys - manage authentication for a resource - secure Cognitive Services by using Azure Virtual Network - plan for a solution that meets responsible AI principles |
| Create a Cognitive Services resource | - create a Cognitive Services resource - configure diagnostic logging for a Cognitive Services resource - manage Cognitive Services costs - monitor a cognitive service - implement a privacy policy in Cognitive Services |
| Plan and implement Cognitive Services containers | - identify when to deploy to a container - containerize Cognitive Services (including Computer Vision API, Face API, Languages, Speech, Form Recognizer) - deploy Cognitive Services Containers in Microsoft Azure |
Implement Computer Vision Solutions (20-25%) | |
| Analyze images by using the Computer Vision API | - retrieve image descriptions and tags by using the Computer Vision API - identify landmarks and celebrities by using the Computer Vision API - detect brands in images by using the Computer Vision API - moderate content in images by using the Computer Vision API - generate thumbnails by using the Computer Vision API |
| Extract text from images | - extract text from images or PDFs by using the Computer Vision service - extract information using pre-built models in Form Recognizer - build and optimize a custom model for Form Recognizer |
| Extract facial information from images | - detect faces in an image by using the Face API - recognize faces in an image by using the Face API - analyze facial attributes by using the Face API - match similar faces by using the Face API |
| Implement image classification by using the Custom Vision service | - label images by using the Computer Vision Portal - train a custom image classification model in the Custom Vision Portal - train a custom image classification model by using the SDK - manage model iterations - evaluate classification model metrics - publish a trained iteration of a model - export a model in an appropriate format for a specific target - consume a classification model from a client application - deploy image classification custom models to containers |
| Implement an object detection solution by using the Custom Vision service | - label images with bounding boxes by using the Computer Vision Portal - train a custom object detection model by using the Custom Vision Portal - train a custom object detection model by using the SDK - manage model iterations - evaluate object detection model metrics - publish a trained iteration of a model - consume an object detection model from a client application - deploy custom object detection models to containers |
| Analyze video by using Azure Video Analyzer for Media (formerly Video Indexer) | - process a video - extract insights from a video - moderate content in a video - customize the Brands model used by Video Indexer - customize the Language model used by Video Indexer by using the Custom Speech service - customize the Person model used by Video Indexer - extract insights from a live stream of video data |
Implement Natural Language Processing Solutions (20-25%) | |
| Analyze text by using the Language service | - retrieve and process key phrases - retrieve and process entity information (people, places, urls, etc.) - retrieve and process sentiment - detect the language used in text |
| Manage speech by using the Speech service | - implement text-to-speech - customize text-to-speech - implement speech-to-text - improve speech-to-text accuracy - improve text-to-speech accuracy - implement intent recognition |
| Translate language | - translate text by using the Translator service - translate speech-to-speech by using the Speech service - translate speech-to-text by using the Speech service |
| Build a initial language model by using Language Understanding Service (LUIS) | - create intents and entities based on a schema, and add utterances - create complex hierarchical entities
- train and deploy a model |
| Iterate on and optimize a language model by using Language Understanding | - implement phrase lists - implement a model as a feature (i.e. prebuilt entities) - manage punctuation and diacritics - implement active learning - monitor and correct data imbalances - implement patterns |
| Manage a Language Understanding model | - manage collaborators - manage versioning - publish a model through the portal or in a container - export a LUIS package - deploy a LUIS package to a container - integrate Bot Framework (LUDown) to run outside of the LUIS portal |
| Create a Questions Answering solution using the Language service | - create a question answering project - import questions and answers - train and test a knowledge base - publish a knowledge base - create a multi-turn conversation - add alternate phrasing - add chit-chat to a knowledge base- export a knowledge base - add active learning to a knowledge base |
Implement Knowledge Mining Solutions (15-20%) | |
| Implement a Cognitive Search solution | - create data sources - define an index - create and run an indexer - query an index - configure an index to support autocomplete and autosuggest - boost results based on relevance - implement synonyms |
| Implement an enrichment pipeline | - attach a Cognitive Services account to a skillset - select and include built-in skills for documents - implement custom skills and include them in a skillset |
| Implement a knowledge store | - define file projections - define object projections - define table projections - query projections |
| Manage a Cognitive Search solution | - provision Cognitive Search - configure security for Cognitive Search - configure scalability for Cognitive Search |
| Manage indexing | - manage re-indexing - rebuild indexes - schedule indexing - monitor indexing - implement incremental indexing - manage concurrency - push data to an index - troubleshoot indexing for a pipeline |
Implement Conversational AI Solutions (15-20%) | |
| Design and implement conversation flow | - design conversation logic for a bot - create and evaluate *.chat file conversations by using the Bot Framework Emulator - choose an appropriate conversational model for a bot, including activity handlers and dialogs |
| Create a bot by using the Bot Framework SDK | - use the Bot Framework SDK to create a bot from a template - implement activity handlers and dialogs - use Turn Context - test a bot using the Bot Framework Emulator - deploy a bot to Azure |
| Create a bot by using the Bot Framework Composer | - implement dialogs - maintain state - implement logging for a bot conversation - implement prompts for user input - troubleshoot a conversational bot - test a bot - publish a bot - add language generation for a response - design and implement adaptive cards |
| Integrate Cognitive Services into a bot | - integrate a question answering model - integrate a LUIS service - integrate a Speech service resource |
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Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution
Candidates for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution build, manage, and deploy AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.
They work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.
Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.
They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.
Part of the requirements for: Microsoft Certified: Azure AI Engineer Associate
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Our Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) Preparation Material provides you everything you will need to take a Microsoft Azure AI Engineer Associate AI-102日本語 examination. Details are researched and produced by Microsoft Certification Experts who are constantly using industry experience to produce precise, and logical.
Topics of AI-102: Designing and Implementing an Azure AI Solution Exam
Candidates should apprehend the examination topics before they begin of preparation. because it'll extremely facilitate them in touch the core. Our AI-102 exam dumps will include the following topics:
1. Analyze solution requirements (25-30%)
Recommend Cognitive Services APIs to meet business requirements
- Identify components and technologies required to connect service endpoints
- Identify automation requirements
- Select the appropriate AI models and services
- Select the processing architecture for a solution
- Select the appropriate data processing technologies
Map security requirements to tools, technologies, and processes
- Identify which users and groups have access to information and interfaces
- Identify appropriate tools for a solution
- Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
- Identify auditing requirements
Select the software, services, and storage required to support a solution
- Identify appropriate services and tools for a solution
- Identify integration points with other Microsoft services
- Identify storage required to store logging, bot state data, and Cognitive Services output
2. Design AI solutions (40-45%)
Design solutions that include one or more pipelines
- Define an AI application workflow process
- Design the integration point between multiple workflows and pipelines
- Design a strategy for ingest and egress data
- Design pipelines that use AI apps
- Select an AI solution that meet cost constraints
- Design pipelines that call Azure Machine Learning models
Design solutions that uses Cognitive Services
- Design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs
Design solutions that implement the Bot Framework
- Design bots that integrate with channels
- Integrate bots and AI solutions
- Design bot services that use Language Understanding (LUIS)
- Integrate bots with Azure app services and Azure Application Insights
Design the compute infrastructure to support a solution
- Identify whether to create a GPU, FPGA, or CPU-based solution
- Select a compute solution that meets cost constraints
- Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
Design for data governance, compliance, integrity, and security
- Define how users and applications will authenticate to AI services
- Design a content moderation strategy for data usage within an AI solution
- Design strategies to ensure that the solution meets data privacy regulations and industry standards
- Ensure appropriate governance of data
- Ensure that data adheres to compliance requirements defined by your organization
3. Implement and monitor AI solutions (25-30%)
Implement an AI workflow
- Manage the flow of data through the solution components
- Create solution endpoints
- Develop AI pipelines
- Develop streaming solutions
- Implement data logging processes
- Define and construct interfaces for custom AI services
Integrate AI services with solution components
- Implement Azure Search in a solution
- Configure integration with Cognitive Services
- Configure prerequisite components to allow connectivity to the Bot Framework
- Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs
Monitor and evaluate the AI environment
- Recommend changes to an AI solution based on performance data
- Identify the differences between KPIs, reported metrics, and root causes of the differences
- Identify the differences between expected and actual workflow throughput
- Maintain an AI solution for continuous improvement
- Monitor AI components for availability
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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