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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| 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 Python application collects customer comments before posting them to a public forum.
You need to send a text comment to Azure AI Content Safety and return the self-harm severity from the response.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: AnalyzeTextOptions(text=comment)
To set up the request in your Python script, you must use AnalyzeTextOptions(text=comment), passing the text as a single string rather than a list.
Example:
Build the request options focusing on Text analysis
# You can optionally restrict analysis to specific categories
request_options = AnalyzeTextOptions(
text=user_comment,
categories=[TextCategory.SELF_HARM]
Box 2: client.analyze_text(request)
You must use client.analyze_text(request)
1. Send the request: Call client.analyze_text(request) to retrieve the multi-severity results.
2. Extract the self-harm result: Locate the self-harm categories from the response list.
3. Return the severity: Access the .severity attribute
Reference:
https://learn.microsoft.com/en-us/python/api/overview/azure/ai-contentsafety-readme
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
Hotspot Question
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
- Authenticates by using a Microsoft Entra managed identity
- Sends prompts to a deployed model by using the Azure OpenAI Responses API How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
You have a Microsoft Foundry project that serves a high-volume chat app.
Most requests are simple FAQs, but some require advanced reasoning.
You need to reduce costs and latency for common queries, without degrading the quality of the responses to complex questions.
What should you do?
- A. Route all the requests to a smaller model.
- B. Increase the value of the max_tokens parameter for all the requests.
- C. Route all the requests to the most capable model.
- D. Use a model cascade that routes the requests to different models.
Correct Answer: D 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
You are building a chatbot.
You need to ensure that the chatbot can classify user input into separate categories. The categories must be dynamic and defined at the time of inference.
Which service should you use to classify the input?
- A. Azure AI Language custom named entity recognition (NER)
- B. Azure OpenAI text classification
- C. Azure OpenAI text summarization
- D. Azure AI Language custom text classification
Correct Answer: B 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
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