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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
1. You have an application that uses a prebuilt Azure Language model.
You need to generate a summary that meets the following requirements:
- Summarizes documents that contain multiple sentences
- Preserves the original sentence order
- Returns exactly three sentences
Which Language service feature should you use?
A) extractive summarization
B) key phrase extraction
C) abstractive summarization
D) sentiment analysis
2. 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) hybrid search
C) keyword search only
D) vector search only
3. You have a Microsoft Foundry project that contains an agent.
The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.
You use Azure Content Understanding in Foundry Tools to process the PDFs.
You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.
Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.
You need to ensure that the Content Understanding document analyzer output includes a per- field confidence score and source grounding to locations within the source document.
What should you do?
A) Enable estimateFieldSourceAndConfidence.
B) Set enableSegment to true.
C) Configure the analyzer to use generative extraction for all fields.
D) Provide labeled samples.
4. You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools.
You fine-tune a baseline speech to text model for the en-us locale and publish the model.
The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid.
You need to set the project property to the correct ID.
To what should you set the project property?
A) the custom speech project ID
B) the project ID
C) the project URL
D) the custom speech endpoint URL
5. You have an Azure AI Search indexer that ingests PDF policy manuals.
Client applications must display page-level citations that have bounding polygons for both text and images.
You need to add a single built-in multimodal content extraction skill to the Azure AI Search skillset. The solution must meet the following requirements:
- Provide text and image location metadata.
- Extract tables that span multiple pages.
What should you add?
A) Azure Content Understanding in Foundry Tools
B) Document Layout
C) Document Extraction
D) GenAI Prompt
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: A |





