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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question 1

You have a product support manual.
You need to build a product support chatbot based on the manual. The solution must minimize development effort and costs.
What should you use?

A. Azure AI Language Custom question answering
B. Azure AI Phi-3-medium with fine-tuning
C. Azure OpenAI GPT-4 with grounding data that uses Azure AI Search
D. Azure AI Document intelligence

Question 2

Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 3

Hotspot Question
You have a Microsoft Foundry project that contains an internal Q&A agent.
Users report the following issues when they ask the agent questions:
- An increase in the following response: "No relevant information
found"
- Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? 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 project.
You plan to build a customer support solution that contains an agent. The solution must meet the following requirements:
- Provide accurate, context-aware responses grounded in internal
product documentation stored in Azure AI Search.
- Require deep, multi-step reasoning across long contexts.
- Generate detailed natural language responses.
Which type of model should you use to power the agent?

A. a multimodal model
B. a small language model (SLM)
C. a large language model (LLM)
D. a key phrase extraction model

Question 5

You have a Microsoft Foundry project that contains an agent.
The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.
You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page-number metadata.
Users frequently ask questions that require the retrieval of specific table rows across the pages.
You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.
How should you configure the ingestion job?

A. Use OCR and page-level chunking.
B. Use page-level OCR extraction and store each page as a single chunk.
C. Use basic parsing and fixed-size chunking.
D. Use advanced data parsing to reingest the documents.

Solutions: