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For more info read reference:
microsoft learning site AI-102 Skills measured Publish a Machine Learning Experiment with Microsoft Azure Machine Learning Studio Process and translate speech with Azure Cognitive Speech Services
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Prerequisites
Before enrolling in the process of taking the AI-102 exams, candidates should be skilled in implementing Python and C#, using APIs and SDKs based on REST to create natural language processing solutions, computer vision solutions, and knowledge mining and communicative AI solutions based on Azure. In addition, such specialists should be knowledgeable of the elements that create the Azure AI portfolio as well as the data storage options. To add more, they should be able to implement AI principles appropriately.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102
How to book the AI-102: Designing and Implementing an Azure AI Solution Exam
These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.
- Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
- Step 2: Sign up/Login to Pearson VUE account
- Step 3: Select local centre based on your country, date, time and confirm with a payment method.
Microsoft AI-102 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI solutions | 15-20% | - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Deploy and manage generative models |
| Topic 2: Implement computer vision solutions | 10-15% | - Integrate vision capabilities into applications - Build and deploy custom vision models - Extract text and handwriting from images - Process and index video content - Analyze images and detect objects/features |
| Topic 3: Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Ingest and process structured/unstructured data |
| Topic 4: Implement natural language processing solutions | 15-20% | - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Implement translation and summarization |
| Topic 5: Plan and manage an Azure AI solution | 20-25% | - Select suitable AI models - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Select appropriate Microsoft Foundry Services - Monitor, optimize, and secure AI solutions |
| Topic 6: Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration |
Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions:
You have an Azure subscription that contains an Azure Al Search instance named AlSearch1. AlSearch1 contains an index that includes a vector.
You need to perform the following actions:
* Deploy a new agent by using the Azure Al Agent Service.
* Connect the AlSearch1 index to the new agent.
* Validate the integration of the index and the agent.
Which four 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:
To deploy an agent and connect it to an existing Azure AI Search index (which already contains vectors), you first need the connection ID for the search resource. Using the Azure AI Projects/Agents client, you obtain that connection ID from your project's connections. Next, you configure the Azure AI Search tool by specifying the index_connection_id , index_name , and (optionally) the query type (simple, semantic, vector, or hybrid), which supports vectorized indexes. After the tool is configured, you create the agent with the tool enabled (passing tools and tool_resources ). Finally, you validate the integration by starting a thread and asking questions grounded in the Azure AI Search index. This sequence matches the Microsoft guidance and minimizes effort because it reuses the existing index and only adds a configured tool to a new agent.
Microsoft References
Use an existing index with the Azure AI Search tool (sequence: create client # configure tool # create agent with tool # ask questions). Microsoft Learn Azure AI Search tool overview (add the tool to an agent; supported search types including vector and hybrid).
Microsoft Learn
AzureAISearchTool and AzureAISearchQueryType (parameters, including connection ID and query types:
simple, semantic, vector, hybrid).
Vector and hybrid search in Azure AI Search (background for vector-enabled indexes and hybrid queries).
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You build a language model by using a Language Understanding service. The language model is used to search for information on a contact list by using an intent named FindContact.
A conversational expert provides you with the following list of phrases to use for training.
Find contacts in London. Who do I know in Seattle?
Search for contacts in Ukraine.
You need to implement the phrase list in Language Understanding.
Solution: You create a new pattern in the FindContact intent.
Does this meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
Explanation: Only visible for DumpsValid members. You can sign-up / login (it's free).
You have a text-based chatbot.
You need to enable content moderation by using the Text Moderation API of Content Moderator. Which two service responses should you use? Each correct answer presents part of the solution NOTE: Each correct selection is worth one point.
- A. optical character recognition (OCR)
- B. text classification
- C. the racy classification score
- D. the adult classification score
- E. personal data
Correct Answer: C,D 🗳️
Explanation: Only visible for DumpsValid members. You can sign-up / login (it's free).
You are building a chatbot.
You need to use the Content Moderator service to identify messages that contain sexually explicit language.
Which section in the response from the service will contain the category score, and which category will be assigned to the message? To answer, select the appropriate options in the answer area, NOTE: Each correct selection is worth one point.
Correct Answer:

Explanation:
Comprehensive Detailed Explanation
The scenario:
You are using Azure Content Moderator to filter chatbot messages.
You must detect sexually explicit language.
The question asks: which section of the response provides the category score, and which category number corresponds to sexual content.
Step 1 - Understand Content Moderator response structure
Content Moderator returns several sections:
Classification # Provides scores for categories of potentially unsafe or inappropriate content.
PII # Identifies personally identifiable information (phone numbers, email, addresses, etc.).
Terms # Identifies terms from a custom or built-in blocklist.
Since the requirement is sexually explicit language detection, the correct section is Classification.
Step 2 - Identify the correct Category
Content Moderator's Classification section returns three default categories:
Category 1: Potentially sexually explicit or adult content.
Category 2: Potentially sexually suggestive content.
Category 3: Offensive or derogatory language.
Since the question specifies sexually explicit language, the correct category is 1.
Correct Answer Mapping:
Section: Classification
Category: 1
Microsoft References
Content Moderator - Text moderation (classification categories explained) Content Moderator overview and response schema
You have a chatbot that was built by using Microsoft Bot Framework and deployed to Azure.
You need to configure the bot to support voice interactions. The solution must support multiple client apps.
Which type of channel should you use?
- A. Direct Line Speech
- B. Cortana
- C. Microsoft Teams
Correct Answer: A 🗳️
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