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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 2: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 3: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 4: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 5: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
Hotspot Question
You have a Microsoft Founcy project that contains a Retrieval Augmented Generation (RAG) solution.
You need to run a pre-production evaluation by using labeled CSV dataset that contains the query, context, response and ground truth. The evaluation must measure the following:
- Whether responses address the user query
- Whether responses are supported by the provided context
- Whether responses contain sensitive or proprietary information
Which AI quality evaluation metrics should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Groundedness and Relevance
The two best AI quality evaluation metrics for your pre-production evaluation in Microsoft Foundry are Relevance and Groundedness.
Microsoft Foundry provides built-in, AI-assisted quality evaluators specifically designed to measure these two critical dimensions of a Retrieval-Augmented Generation (RAG) system:
Relevance: This metric directly addresses your first requirement: whether responses address the user query. It evaluates how well the gens.nerated response aligns with and answers the initial user query.
Groundedness: This metric directly addresses your second requirement: whether responses are supported by the provided context. It evaluates the degree to which the generated response is factually supported by and derived strictly from the retrieved context, which helps capture model hallucinatio Box 2: Protected material The best AI quality evaluation metric for this scenario is Protected material.
Direct alignment: The Protected material metric specifically checks whether an AI model outputs copyrighted text, proprietary data, or sensitive information that matches its training data or internal context.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/langchain-middleware
You have an Azure subscription.
You plan to build an app that will use the Azure AI DALL-E model.
You need to deploy the model.
What should you use?
- A. the Azure SDK for JavaScript and Azure Machine Learning Studio.
- B. the Azure portal and Microsoft Graph API
- C. the Azure SDK for Python and PowerShell cmdlets.
- D. Microsoft Foundry and the Azure Command Line Interface (CLI)
Correct Answer: D 🗳️
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You are building an image sharing app that will use Azure AI to prevent users from sharing sexually explicit images.
You need to ensure that inappropriate images are identified correctly. The solution must minimize development effort.
What should you use?
- A. Vision Studio in Azure AI Vision
- B. Azure AI Content Safety Studio
- C. Visual Studio
- D. Azure AI Studio
Correct Answer: B 🗳️
Explanation: Only visible for Dumps4PDF members. You can sign-up / login (it's free).
You have a Microsoft Foundry project that contains a customer support agent. The agent calls an internal knowledge API tool before generating responses.
Users report the following issues:
- Some requests take more than 15 seconds to complete.
- Some responses are incorrect, even when the knowledge API returns the expected data.
You need to inspect individual agent runs to view the ordered sequence of large language model (LLM) calls, tool invocations, and timing information.
Which observability capability should you use?
- A. tracing
- B. monitoring
- C. token usage
- D. safety metrics
Correct Answer: A 🗳️
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You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of all the files that can be uploaded to the service?
- A. 1 GB
- B. 1 TB
- C. 100 GB
- D. 10 GB
Correct Answer: C 🗳️
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Viewing questions 1-5 out of 159 questions.
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