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Microsoft Designing and Implementing a Microsoft Azure AI Solution : AI-102
- Exam Code: AI-102
- Exam Name: Designing and Implementing a Microsoft Azure AI Solution
- Updated: Oct 14, 2026 Q & A: 425 Questions and Answers
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What is the duration, language, and format of AI-102: Designing and Implementing an Azure AI Solution Exam
- Language: English, Japanese, Chinese (Simplified), Korean.
- This is beta exam.
- Length of Examination: 50 mins
- Passing Score: 700 / 1000
- Type of Questions: This test format is multiple choice.
- Number of Questions: 40 to 60 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice.)
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
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The price of the Microsoft Mobility and Devices Fundamentals exam is $165 USD, for more information related to exam price please visit to Microsoft Training website as prices of Microsoft exams fees get varied country wise.
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Microsoft AI-102 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement Natural Language Processing Solutions | 20-25% | - Translate text and speech - Implement speech capabilities using Azure AI Speech - Analyze text using Azure AI Language - Build question answering solutions - Create conversational AI solutions with Azure AI Bot Service |
| Implement Decision Support Solutions | 10-15% | - Design and implement decision automation - Integrate Azure AI services into applications - Monitor and optimize decision support solutions |
| Plan and Manage an Azure AI Solution | 15-20% | - Select appropriate Azure AI services and technologies - Manage AI costs and resource allocation - Implement governance and compliance for AI solutions - Plan and configure secure AI solutions |
| Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement Azure AI Search solutions - Create custom skills for Azure AI Search - Implement semantic and vector search - Design document processing pipelines |
| Implement Generative AI Solutions | 10-15% | - Integrate Azure OpenAI models into applications - Apply responsible AI practices - Use Azure OpenAI Service for generative AI - Implement prompt engineering techniques |
| Implement Computer Vision Solutions | 15-20% | - Read and process documents using Azure AI Document Intelligence - Implement Azure AI Face service - Analyze images using Azure AI Vision - Process video and generate insights - Implement object detection and image classification |
Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions:
Question #1
You are examining the Text Analytics output of an application.
The text analyzed is: " Our tour guide took us up the Space Needle during our trip to Seattle last week. " The response contains the data shown in the following table.
Which Text Analytics API is used to analyze the text?
- A. Key Phrase Extraction
- B. Sentiment Analysis
- C. Named Entity Recognition
- D. Entity Linking
Correct Answer: C 🗳️
Explanation: Only visible for Pass4guide members. You can sign-up / login (it's free).
Question #2
You have a Language Understanding solution that runs in a Docker container.
You download the Language Understanding container image from the Microsoft Container Registry (MCR).
You need to deploy the container image to a host computer.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
You are deploying a Language Understanding (LUIS) container. When running LUIS in Docker, the model must first be exported from the Language Understanding portal, and then provided to the container at runtime.
Step-by-step reasoning:
From the Language Understanding portal, export the solution as a package file.
The trained LUIS model must be exported from the portal into a .json package file.
This is required because the container cannot access the hosted service directly.
From the host computer, move the package file to the Docker input directory.
Containers expect the model to be available locally.
The package file is placed into the input directory that the container maps for models.
From the host computer, run the container and specify the input directory.
When starting the container, you specify --volume < local_input > :/input so the container has access to the package file.
This makes the exported model available inside the container for processing.
Why not the other options?
Retain the model in the portal is not sufficient; the container cannot pull directly from the cloud.
Build the container and specify the output directory is not required; the container image is already available from MCR and is not custom-built for this step.
Correct Answer Order:
Export the solution as a package file.
Move the package file to the Docker input directory.
Run the container and specify the input directory.
Microsoft References
Run LUIS containers
Use containers with Azure AI services
Question #3
You have an Azure Cognitive Search resource named Search 1 that is used by multiple apps You need to secure Search 1. The solution must meet the following requirements:
* Prevent access to Search1 from the internet.
* Limit the access of each app to specific queries
What should you do? To answer, select the appropriate options in the answer area NOTE Each correct answer is worth one point.
Correct Answer:
Explanation:
You are securing an Azure Cognitive Search resource with two specific requirements:
Prevent access to Search1 from the internet
The correct way is to use a private endpoint.
A private endpoint connects the Cognitive Search resource directly into your virtual network (VNet), blocking public internet access.
Configuring an IP firewall can restrict access to specific IP ranges but does not fully prevent internet access.
Azure roles and authentication do not address network-level restrictions.
Limit the access of each app to specific queries
This is done via Azure role-based access control (RBAC).
You can assign different Azure roles or API keys with query rights to control what each app can do.
Key authentication alone only provides coarse-grained access (admin/query keys) but does not allow fine- grained per-app restrictions.
RBAC ensures principle of least privilege is enforced.
The answer:
Prevent access from internet # Create a private endpoint
Limit access to queries # Use Azure roles
Microsoft References
Secure access to Azure Cognitive Search using private endpoints
Role-based access control in Azure Cognitive Search
Question #4
You need to ensure that the chatbot can classify user input into separate categories. The categones must be dynamic and defined at the time of inference.
Which service should you use to classify the input?
- A. Azure Al Language custom named entity recognition (NER)
- B. Azure OpenAI text classification
- C. Azure Al Language custom text classification
- D. Azure OpenAI text summarization
Correct Answer: B 🗳️
Explanation: Only visible for Pass4guide members. You can sign-up / login (it's free).
Question #5
You are building a language learning solution.
You need to recommend which Azure services can be used to perform the following tasks:
* Analyze lesson plans submitted by teachers and extract key fields, such as lesson times and required texts.
* Analyze learning content and provide students with pictures that represent commonly used words or phrases in the text The solution must minimize development effort.
Which Azure service should you recommend for each task? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Analyze lesson plans and extract key fields
Use Azure AI Document Intelligence (Form Recognizer) to automatically extract structured data (dates/times, required texts, names, etc.) from uploaded lesson-plan documents (PDFs/images). It provides prebuilt and custom extraction with minimal code via Document Intelligence Studio.
Analyze learning content and show pictures for common words/phrases
Use Immersive Reader, which includes features like Picture Dictionary and text comprehension supports directly in apps with minimal development. It can highlight words and show illustrative images to aid vocabulary learning-exactly the requirement.
Other options are not a fit:
Azure AI Custom Vision is for training image classifiers/detectors, not text comprehension.
Azure AI Search indexes and searches content; it doesn't provide picture dictionary/reading experience.
Microsoft Azure AI References
Document Intelligence (Form Recognizer) overview & extraction capabilities: learn.microsoft.com/azure/ai- services/document-intelligence/overview Immersive Reader - Picture Dictionary & reading features: learn.microsoft.com/azure/ai-services/immersive- reader/overview
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