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 |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions:
You are building content for a video training solution.
You need to create narration to accompany the video content. The solution must use Custom Neural Voice.
What should you use to create a custom neural voice, and which service should you use to generate the narration? To answer, select the appropriate options in the answer area.
NOTE: Each correct answer is worth one point.
Correct Answer:
Explanation:
To build narration for video training content using Custom Neural Voice, we must know where each step is done:
Creating a Custom Neural Voice
A Custom Neural Voice is a text-to-speech (TTS) feature within Azure AI Speech.
It allows you to create a unique, natural-sounding synthetic voice.
The correct tool to build and configure a custom voice is the Speech Studio portal.
Not the Language Understanding portal (LUIS), Azure portal directly, or Bot Framework Composer.
Generating narration
Narration requires converting written text into speech.
This is done via the Text-to-Speech service in Azure AI Speech.
Other options:
Language Understanding # For intent/utterance processing, not narration.
Speaker Recognition # For identifying/authenticating speakers, not narration.
Speech-to-text # Converts spoken words to text (the opposite direction).
Thus:
Custom Neural Voice # Speech Studio portal
Narration # Text-to-speech
Correct Answers:
Custom neural voice: The Speech Studio portal
Narration: Text-to-speech
Microsoft References
Custom Neural Voice in Speech Studio
Text-to-Speech overview
You are developing a photo application that will find photos of a person based on a sample image by using the Face API.
You need to create a POST request to find the photos.
How should you complete the request? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: findsimilars
https://docs.microsoft.com/en-us/rest/api/faceapi/face/find-similar
Box 2: matchPerson
Find similar has two working modes, " matchPerson " and " matchFace " . " matchPerson " is the default mode that it tries to find faces of the same person as possible by using internal same-person thresholds. It is useful to find a known person ' s other photos. Note that an empty list will be returned if no faces pass the internal thresholds. " matchFace " mode ignores same-person thresholds and returns ranked similar faces anyway, even the similarity is low. It can be used in the cases like searching celebrity-looking faces.
Reference:
https://docs.microsoft.com/en-us/rest/api/faceapi/face/detectwithurl
https://docs.microsoft.com/en-us/rest/api/faceapi/face/findsimilar
You are building a message handling system that will use the Azure Translator in Foundry Tools service.
You need to ensure that incoming messages are translated to English.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You have a collection of 50,000 scanned documents that contain text.
You plan to make the text available through Azure Cognitive Search.
You need to configure an enrichment pipeline to perform optical character recognition (OCR) and text analytics. The solution must minimize costs.
What should you attach to the skillset?
- A. a new Computer Vision resource
- B. a free (Limited enrichments) Cognitive Services resource
- C. a new Cognitive Services resource that uses the SO pricing tier
- D. an Azure Machine Learning pipeline
Correct Answer: A 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
You are building an app that will perform translations by using the Azure Al Translator service.
You need to ensure that the app will translate user-inputted text.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
In the Azure AI Translator .NET SDK ( Azure.AI.Translation.Text ), the TextTranslationClient exposes TranslateAsync overloads that accept a target language (e.g., " fr " ) and an input string to translate. The call returns a Response < IReadOnlyList < TranslatedTextItem > > . Each TranslatedTextItem includes the automatically detected source language ( DetectedLanguage.Language ) and a collection of translations in Translations . To print the translated text for the first target language, read translation.Translations[0].Text .
Therefore:
Use TranslateAsync(targetLanguage, input) to perform the translation.
Output translation.Translations[0].Text to display the translated result.
Microsoft References
TextTranslationClient.TranslateAsync method (overloads, usage). Microsoft Learn TextTranslationClient class overview. Microsoft Learn TranslatedTextItem model and Translations property (array of translation results).
Quickstart: Translator text client libraries (end-to-end usage pattern). Microsoft Learn
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