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Microsoft AI-102 Exam Topics, Blueprint and Syllabus

Designing and Implementing a Microsoft Azure AI Solution

Last Update March 1, 2024
Total Questions : 267

Our Azure AI Engineer Associate AI-102 exam questions and answers cover all the topics of the latest Designing and Implementing a Microsoft Azure AI Solution exam, See the topics listed below. We also provide Microsoft AI-102 exam dumps with accurate exam content to help you prepare for the exam quickly and easily. Additionally, we offer a range of Microsoft AI-102 resources to help you understand the topics covered in the exam, such as Azure AI Engineer Associate video tutorials, AI-102 study guides, and AI-102 practice exams. With these resources, you can develop a better understanding of the topics covered in the exam and be better prepared for success.

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Microsoft AI-102 Exam Overview :

Exam Name Designing and Implementing a Microsoft Azure AI Solution
Exam Code AI-102
Actual Exam Duration The duration of the Microsoft AI-102 exam is 180 minutes (3 hours).
What exam is all about Microsoft AI-102 is an exam that tests the candidate's knowledge and skills in designing and implementing AI solutions using Microsoft Azure technologies. The exam covers various topics such as natural language processing, computer vision, speech recognition, and machine learning. The exam is intended for professionals who have experience in developing AI solutions and want to validate their skills and knowledge in this area. Passing the Microsoft AI-102 exam leads to the Microsoft Certified: Azure AI Engineer Associate certification.
Passing Score required The passing score required in the Microsoft AI-102 exam is 700 out of 1000. This means that you need to answer at least 70% of the questions correctly to pass the exam. The actual passing score may vary depending on the difficulty level of the exam and the number of questions included in it. It is important to note that the passing score is subject to change without prior notice, so it is best to check the official Microsoft website for the latest information.
Competency Level required Based on the official Microsoft documentation, the AI-102 exam is designed for Azure AI Engineers who have intermediate to advanced knowledge and experience in designing and implementing AI solutions using Azure services. Candidates should have a good understanding of machine learning models, natural language processing, computer vision, and conversational AI. They should also be proficient in programming languages such as Python and have experience working with Azure Machine Learning, Azure Cognitive Services, and Azure Bot Service. Additionally, candidates should have experience in deploying and managing AI solutions on Azure.
Questions Format Based on the exam objectives and previous Microsoft certification exams, the AI-102 exam may include multiple-choice questions, drag-and-drop questions, and scenario-based questions that require candidates to analyze and solve real-world problems using AI technologies. The exam may also include simulations or practical tasks that test candidates' ability to implement and deploy AI solutions using Microsoft Azure services.
Delivery of Exam The Microsoft AI-102 exam is a computer-based exam that is delivered online through the Microsoft Learning platform. It is a timed exam that consists of multiple-choice questions and scenario-based questions. The exam is designed to test the candidate's knowledge and skills in designing and implementing AI solutions using Microsoft Azure services. The exam duration is 180 minutes, and the passing score is 700 out of 1000.
Language offered English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, Indonesian (Indonesia)
Cost of exam $165 USD
Target Audience The target audience for Microsoft AI-102 certification exam includes: 1. AI developers who want to enhance their skills in designing and implementing AI solutions using Microsoft Azure services. 2. Data scientists who want to learn how to use Azure AI services to build intelligent applications. 3. Solution architects who want to design and implement AI solutions using Azure services. 4. Technical leads who want to lead AI projects and teams using Azure services. 5. Business analysts who want to understand how AI can be used to solve business problems using Azure services. 6. IT professionals who want to learn how to integrate AI solutions with existing systems and infrastructure using Azure services. 7. Developers who want to learn how to build intelligent applications using Azure services. 8. Anyone who wants to learn about AI and its applications in the Microsoft Azure ecosystem.
Average Salary in Market According to Payscale, the average salary for a Microsoft Certified Azure AI Engineer Associate is around $120,000 per year in the United States. This may vary depending on factors such as location, experience, and industry.
Testing Provider You can visit the official Microsoft website to register for the exam and get more information about it. Additionally, you can also check out various online platforms that offer practice tests and study materials for the AI-102 exam.
Recommended Experience based on the information available, the recommended experience for the Microsoft AI-102 exam includes: 1. Experience with Azure Machine Learning and Azure Cognitive Services. 2. Knowledge of data science and machine learning concepts, including supervised and unsupervised learning, feature engineering, and model evaluation. 3. Familiarity with programming languages such as Python and R. 4. Understanding of natural language processing (NLP) and computer vision (CV) concepts. 5. Experience with deploying and managing machine learning models in production environments. 6. Knowledge of Azure services such as Azure Functions, Azure Logic Apps, and Azure Event Grid. 7. Familiarity with DevOps practices and tools for machine learning, such as Azure DevOps and GitHub. 8. Understanding of ethical and responsible AI practices, including fairness, transparency, and privacy. It is important to note that these are only recommendations, and the actual experience required may vary depending on the individual's background and expertise.
Prerequisite According to Microsoft's official website, the prerequisites for the AI-102 exam are: 1. A fundamental understanding of Azure services, including Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service. 2. Experience in developing solutions that use Azure services. 3. Knowledge of Python programming language and machine learning concepts. 4. Familiarity with data science and data engineering concepts. 5. Understanding of DevOps practices and principles. It is recommended that candidates have at least one year of experience in developing AI solutions using Azure services before taking the AI-102 exam.
Retirement (If Applicable) Microsoft usually provides advance notice before retiring any exam, and candidates are encouraged to check the Microsoft certification website for updates on exam retirements.
Certification Track (RoadMap): The Microsoft AI-102 exam is a certification exam that focuses on designing and implementing AI solutions using Microsoft Azure technologies. The certification track/roadmap for the AI-102 exam includes the following steps: 1. Learn the basics of AI and machine learning concepts. 2. Gain knowledge of Azure services and tools for AI development. 3. Develop skills in designing and implementing AI solutions using Azure services. 4. Prepare for the AI-102 exam by studying the exam objectives and taking practice tests. 5. Pass the AI-102 exam to earn the Microsoft Certified: Azure AI Engineer Associate certification. 6. Continue learning and staying up-to-date with the latest AI technologies and trends to maintain the certification.
Official Information https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
See Expected Questions Microsoft AI-102 Expected Questions in Actual Exam
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Microsoft AI-102 Exam Topics :

Section Weight Objectives
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, Text Analytics, Speech, Form Recognizer)
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 by using the OCR API
  • extract text from images or PDFs by using the Read API
  • convert handwritten text by using Ink Recognizer
  • extract information from forms or receipts by using the pre-built receipt model in Form Recognizer
  • build andoptimize 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 customobject 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 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 Text Analytics service
  • retrieve and process key phrases
  • retrieveand 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
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 an initial language model by using Language Understanding Service (LUIS)
  • create intents and entities based on a schema, and then add utterances
  • create complex hierarchical entitiesouse this instead of roles
  • train and deploy a model
Iterate on and optimize a language model by using LUIS
  • 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 LUIS 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
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% Create a knowledge base by using QnA Maker
  • create a QnA Maker service
  • create a knowledge base
  • import a knowledge base
  • 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
  • manage collaborators
Design and implement conversation flow
  • design conversation logic for a bot
  • create and evaluate *.chat file conversations by using the Bot Framework Emulator
  • add language generation for a response
  • design and implement adaptive cards
Create a bot by using the Bot Framework SDK
  • implement dialogs
  • maintain state
  • implement logging for a bot conversation
  • implement a prompt for user input
  • add and review bot telemetry
  • implement a bot-to-human handoff
  • troubleshoot a conversational bot
  • add a custom middleware for processing user messages
  • manage identity and authentication
  • implement channel-specific logic
  • publish a bot
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 by using the Bot Framework Emulator
  • publish a bot

Integrate Cognitive Services into a bot
  • integrate a QnA Maker service
  • integrate a LUIS service
  • integrate a Speech service
  • integrate Dispatch for multiple language models
  • manage keys in app settings file