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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
Question #1
You are designing a content management system.
You need to ensure that the reading experience is optimized for users who have reduced comprehension and learning differences, such as dyslexia. The solution must minimize development effort.
Which Azure service should you include in the solution?
A. Azure Document Intelligence in Foundry Tools
B. Azure Language in Foundry Tools
C. Azure AI Immersive Reader
D. Azure Translator in Foundry Tools
Question #2
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search for Retrieval Augmented Generation (RAG).
You plan to ingest and index PDF product manuals.
You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.
Which indexing approach should you use?
A. suggesters
B. analyzers
C. semantic ranking
D. vector search
Question #3
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.
Question #4
You have a Microsoft Foundry agent that grounds responses from an Azure Search index that contains the following:
- Searchable text fields for product names and product codes
- A vector field that stores embeddings for product descriptions
You need to ensure that users can query the index by using the following:
- Exact product names or codes
- Natural language descriptions of the products
What should you configure?
A. semantic search only
B. vector search only
C. keyword search only
D. hybrid search
Question #5
Hotspot Question
You are building a model to detect objects in images.
The performance of the model based on training data is shown in the following exhibit.
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.
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: D | Question #3 Correct Answer: Only visible for members | Question #4 Correct Answer: D | Question #5 Correct Answer: Only visible for members |


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