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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 2: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 3: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 4: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 5: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
You have an application that uses a prebuilt Azure Language model.
You need to generate a summary that meets the following requirements:
- Identify each presenter in the video and attribute each text file to
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
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
A finance team must extract named fields, such as invoice number and total, from invoices, and they need a per-field confidence score so that low-confidence values can be routed to a human reviewer. Which Azure Content Understanding mode should they use?
- A. Pro mode
- B. Neither, because confidence scoring requires Azure AI Document Intelligence
- C. Either mode, because both return confidence scores
- D. Standard mode
Correct Answer: D 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed fine items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
- Extracts the invoice number, invoice date, vendor name, and total
amount across varying templates
- Returns confidence scores so that results with confidence below 0.80
can be routed for supervisor review
What should you use?
- A. the Azure Content Understanding in Foundry Tools prebuilt-documentSearch analyzer and search.score from the Azure AI Search results for routing
- B. a Foundry agent that has groundedness guardrails enabled to extract invoice fields and confidence scores
- C. the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer
- D. a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing
Correct Answer: D 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAI model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?
- A. Implement a retry policy that uses exponential backoff and jitter.
- B. Create a new thread and retry the calls immediately.
- C. Spit uploaded content into smaller files.
- D. Reduce the number of registered tools.
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
Hotspot Question
You have a Microsoft Foundry project.
- You need to create a customer support agent that meets the following
requirements:
- Grounds responses only in company policy documents stored in curated
repositories
- Retains customer preferences across separate chat sessions
How should you configure the agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Configure retrieval from approved data sources
You should configure retrieval from approved data sources to meet this requirement.
In Microsoft Foundry (and broader Azure AI Foundry / Azure OpenAI architectures), grounding an agent exclusively in company policy documents requires implementing a Retrieval-Augmented Generation (RAG) pattern. This ensures the AI model only answers using the provided context and does not hallucinate or rely on its public training data.
Box 2: Enable agent memory that uses persistent storage
To retain user preferences across completely separate chat sessions or conversations, you must use persistent agent memory. Traditional chat history only tracks messages within a single active thread or session. By contrast, the Memory Store feature provides a managed, long-term memory system that structurally indexes user preferences and facts across different devices and separate sessions.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-new-capabilities-for-azure-openai-on-your-data/4144636
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