AI-102: Microsoft Azure AI Solution Practice Exams Prep.

Candidates for the Azure AI Engineer Associate certification build, manage, and deploy AI solutions that leverage Azure Cognitive Services and Azure Applied AI services.

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 certification 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.

Microsoft Azure AI Solution Exam Summary:

• Exam Name : Microsoft Azure AI Solution

• Exam Code : AI-102

• Exam Price : 165 (USD)

• Number of Questions: Maximum of 40-60 questions,

• Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,

• Length of Test: 130 Minutes. The exam is available in English and Japanese languages.

• Passing Score: 700 / 1000

• Languages : English at launch. Japanese

• Schedule Exam : Pearson VUE

Microsoft AI-102 Exam Syllabus Topics:

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

Select the appropriate Azure AI service

• 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

Plan and configure security for Azure 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 and manage an Azure AI service

• Create an Azure AI resource

• Configure diagnostic logging

• Manage costs for Azure AI services

• Monitor an Azure AI resource

Deploy Azure AI services

• 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 solutions to detect anomalies and improve content

• 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

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

Analyze images

• 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

• 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

Implement image classification and object detection by using the Custom Vision service, part of Azure Cognitive Services

• Choose between image classification and object detection models

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

• Label images

• Train custom image models, including classifiers and detectors

• Manage training iterations

• Evaluate model metrics

• Publish a trained iteration of a model

• Export a model to run on a specific target

• Implement a Custom Vision model as a Docker container

• Interpret model responses

Process videos

• 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

Implement natural language processing solutions (25–30%)

Analyze text

• Retrieve and process key phrases

• Retrieve and process entities

• Retrieve and process sentiment

• Detect the language used in text

• Detect personally identifiable information (PII)

Process speech

• 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 language

• 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

Build and manage a language understanding model

• Create intents and add utterances

• Create entities

• Train evaluate, deploy, and test a language understanding model

• Optimize a Language Understanding (LUIS) model

• Integrate multiple language service models by using Orchestrator

• Import and export language understanding models

Create a question answering solution

• 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

Implement knowledge mining solutions (5–10%)

Implement a Cognitive Search solution

• 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

Apply AI enrichment skills to an indexer 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 incremental enrichment

Implement conversational AI solutions (15–20%)

Design and implement conversation flow

• Design conversational logic for a bot

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

Build a conversational 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, publish, and maintain a conversational bot

• 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

The AI-102 course is intended for AI engineers, data scientists, and machine learning engineers who want to enhance their skills and capabilities in the field of AI. It is an advanced-level course and requires a solid understanding of AI and machine learning concepts and practices. Candidates who successfully complete the AI-102 course will be able to design and implement advanced AI solutions using Azure AI services and tools.

It is recommended that students have completed the Microsoft AI-900 course or have equivalent knowledge before taking the AI-102 course. The AI-102 course is an advanced-level course and requires a solid understanding of AI and machine learning concepts and practices.