Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version) Exam Questions Can Help You Gain Massive Knowledge of AI-102 Korean Certification

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

Select the appropriate AI models and services
Identify components and technologies required to connect service endpoints
Select the processing architecture for a solution
Select the appropriate data processing technologies
Identify automation requirements

Map security requirements to tools, technologies, and processes

Identify which users and groups have access to information and interfaces
Identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements
Identify appropriate tools for a solution
Identify auditing requirements

Select the software, services, and storage required to support a solution

Identify storage required to store logging, bot state data, and Cognitive Services output
Identify appropriate services and tools for a solution
Identify integration points with other Microsoft services

2. Design AI solutions (40-45%)

Design solutions that include one or more pipelines

Design pipelines that use AI apps
Define an AI application workflow process
Design pipelines that call Azure Machine Learning models
Design a strategy for ingest and egress data
Design the integration point between multiple workflows and pipelines
Select an AI solution that meet cost constraints

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 with Azure app services and Azure Application Insights
Design bot services that use Language Understanding (LUIS)
Integrate bots and AI solutions

Design the compute infrastructure to support a solution

Identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure
Identify whether to create a GPU, FPGA, or CPU-based solution
Select a compute solution that meets cost constraints

Design for data governance, compliance, integrity, and security

Design strategies to ensure that the solution meets data privacy regulations and industry standards
Ensure appropriate governance of data
Design a content moderation strategy for data usage within an AI solution
Define how users and applications will authenticate to AI services
Ensure that data adheres to compliance requirements defined by your organization

3. Implement and monitor AI solutions (25-30%)

Implement an AI workflow

Develop AI pipelines
Develop streaming solutions
Manage the flow of data through the solution components
Create solution endpoints
Define and construct interfaces for custom AI services
Implement data logging processes

Integrate AI services with solution components

Implement Azure Search in a solution
Configure prerequisite components to allow connectivity to the Bot Framework
Configure integration with Cognitive Services
Configure prerequisite components and input datasets to allow the consumption of Cognitive Services APIs

Monitor and evaluate the AI environment

Monitor AI components for availability
Identify the differences between KPIs, reported metrics, and root causes of the differences
Identify the differences between expected and actual workflow throughput
Recommend changes to an AI solution based on performance data
Maintain an AI solution for continuous improvement

Constant Improvement

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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 use this instead of roles - 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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Who should take the AI-102: Designing and Implementing an Azure AI Solution Exam

The AI-102 Exam certification is an internationally-recognized certification which help to have validation for Azure AI Solution Architects who have ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102

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