Microsoft Azure AI-102 - Azure AI Solution - Practice Exams

Plan and manage an Azure AI solution (25–30%)

Implement image and video processing solutions (15–20%)

Implement natural language processing solutions (25–30%)

Implement knowledge mining solutions (5–10%)

Implement conversational AI solutions (15–20%)

Select the appropriate service for a vision solution

Select the appropriate service for a language analysis solution

Select the appropriate service for a decision support solution

Select the appropriate service for a speech solution

Select the appropriate Applied AI services

Manage account keys

Manage authentication for a resource

Secure services by using Azure Virtual Networks

Plan for a solution that meets Responsible AI principles

Create an Azure AI resource

Configure diagnostic logging

Manage costs for Azure AI services

Monitor an Azure AI resource

Determine a default endpoint for a service

Create a resource by using the Azure portal

Integrate Azure AI services into a continuous integration/continuous deployment (CI/CD) pipeline

Plan a container deployment

Implement prebuilt containers in a connected environment

Create a solution that uses Anomaly Detector, part of Cognitive Services

Create a solution that uses Azure Content Moderator, part of Cognitive Services

Create a solution that uses Personalizer, part of Cognitive Services

Create a solution that uses Azure Metrics Advisor, part of Azure Applied AI Services

Create a solution that uses Azure Immersive Reader, part of Azure Applied AI Services

Select appropriate visual features to meet image processing requirements

Create an image processing request to include appropriate image analysis features

Interpret image processing responses

Extract text from images or PDFs by using the Computer Vision service

Convert handwritten text by using the Computer Vision service

Extract information using prebuilt models in Azure Form Recognizer

Build and optimize a custom model for Azure Form Recognizer

Choose between image classification and object detection models

Specify model configuration options, including category, version, and compact

Label images

Train custom image models, including image classification and object detection

Manage training iterations

Evaluate model metrics

Publish a trained model

Export a model to run on a specific target

Implement a Custom Vision model as a Docker container

Interpret model responses

Process a video by using Azure Video Indexer

Extract insights from a video or live stream by using Azure Video Indexer

Implement content moderation by using Azure Video Indexer

Integrate a custom language model into Azure Video Indexer

Retrieve and process key phrases

Retrieve and process entities

Retrieve and process sentiment

Detect the language used in text

Detect personally identifiable information (PII)

Implement and customize text-to-speech

Implement and customize speech-to-text

Improve text-to-speech by using SSML and Custom Neural Voice

Improve speech-to-text by using phrase lists and Custom Speech

Implement intent recognition

Implement keyword recognition

Translate text and documents by using the Translator service

Implement custom translation, including training, improving, and publishing a custom model

Translate speech-to-speech by using the Speech service

Translate speech-to-text by using the Speech service

Translate to multiple languages simultaneously

Create intents and add utterances

Create entities

Train evaluate, deploy, and test a language understanding model

Integrate multiple language service models by using an orchestration workflow

Import and export language understanding models

Create a question answering project

Add question-and-answer pairs manually

Import sources

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

Create a multi-language question answering solution

Create a multi-domain question answering solution

Use metadata for question-and-answer pairs

Provision a Cognitive Search resource

Create data sources

Define an index

Create and run an indexer

Query an index, including syntax, sorting, filtering, and wildcards

Manage knowledge store projections, including file, object, and table projections

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 incremental enrichment

Design conversational logic for a bot

Choose appropriate activity handlers, dialogs or topics, triggers, and state handling for a bot

Create a bot from a template

Create a bot from scratch

Implement activity handlers, dialogs or topics, and triggers

Implement channel-specific logic

Implement Adaptive Cards

Implement multi-language support in a bot

Implement multi-step conversations

Manage state for a bot

Integrate Cognitive Services into a bot, including question answering, language understanding, and Speech service

Test a bot using the Bot Framework Emulator or the Power Virtual Agents web app

Test a bot in a channel-specific environment

Troubleshoot a conversational bot

Deploy bot logic