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Microsoft AI-102 Exam Syllabus Topics:

SectionWeightObjectives
Implement natural language processing solutions15-20%- Build conversational AI and chatbots
- Implement translation and summarization
- Perform text analysis, sentiment detection, and language detection
- Customize and deploy NLP models
Implement knowledge mining and information extraction solutions15-20%- Extract entities, relationships, and key phrases
- Ingest and process structured/unstructured data
- Implement intelligent search and retrieval
- Build knowledge bases and search indexes
Implement computer vision solutions10-15%- Extract text and handwriting from images
- Build and deploy custom vision models
- Analyze images and detect objects/features
- Integrate vision capabilities into applications
- Process and index video content
Implement generative AI solutions15-20%- Apply prompt engineering and fine-tuning
- Deploy and manage generative models
- Integrate Azure OpenAI and other generative models
- Implement model monitoring and feedback
- Orchestrate multiple models and containers
Implement an agentic solution5-10%- Test, deploy, and optimize agents
- Develop multi-agent workflows and orchestration
- Build agents with Microsoft Foundry Agent Service
- Understand agent use cases and types
Plan and manage an Azure AI solution20-25%- Monitor, optimize, and secure AI solutions
- Select appropriate Microsoft Foundry Services
- 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 suitable AI models

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AI-102 Sample Questions, Relevant AI-102 Questions

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Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q223-Q228):

NEW QUESTION # 223
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 are building a chatbot that will use question answering in Azure Cognitive Service for Language.
You upload Doc1.pdf and train that contains a product catalogue and a price list.
During testing, users report that the chatbot responds correctly to the following question: What is the price of <product>?
The chatbot fails to respond to the following question: How much does <product> cost?
You need to ensure that the chatbot responds correctly to both questions.
Solution: from Language Studio, you create an entity for price, and then retrain and republish the model.
Does this meet the goal?

Answer: A


NEW QUESTION # 224
You are building a bot and that will use Language Understanding.
You have a LUDown file that contains the following content.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://github.com/solliancenet/tech-immersion-data-ai/blob/master/ai-exp1/README.md


NEW QUESTION # 225
You are using a Language Understanding service to handle natural language input from the users of a web-based customer agent.
The users report that the agent frequently responds with the following generic response: "Sorry, I don't understand that." You need to improve the ability of the agent to respond to requests.
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. (Choose three.)

Answer:

Explanation:


NEW QUESTION # 226
You are reviewing the design of a chatbot. The chatbot includes a language generation file that contains the following fragment.
# Greet(user)
- ${Greeting()}, ${user.name}
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: No
Example: Greet a user whose name is stored in `user.name`
- ${ welcomeUser(user.name) }
Example: Greet a user whose name you don't know:
- ${ welcomeUser() }
Box 2: No
Greet(User) is a Send a response action.
Box 3: Yes
Reference:
https://docs.microsoft.com/en-us/composer/how-to-ask-for-user-input


NEW QUESTION # 227
You are building an app that will perform speech translation by using the Azure ai Language service.
You need to ensure that the language input to the app is supported.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE; Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

To validate that an input language is supported for translation, call the Translator SDK's GetLanguagesAsync with scope: "translation" to retrieve the supported languages. The call returns a GetLanguagesResult, whose Translation property is a dictionary keyed by language codes (e.g., "en", "it"). You can then check membership with ContainsKey(language). If the key exists, the language is supported; otherwise, it isn't.
This matches the .NET Translator client design:
* TextTranslationClient.GetLanguagesAsync(...) fetches supported languages.
* GetLanguagesResult.Translation is a dictionary of translation-capable languages, so ContainsKey is the correct check.
Microsoft References
* TextTranslationClient.GetLanguagesAsync (overview and signature).
* Languages REST reference - use scope=translation to get languages supported for translation.
Microsoft Learn
* GetLanguagesResult class - Translation dictionary property.


NEW QUESTION # 228
......

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