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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 4: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 5: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
1. 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) a Foundry agent that has groundedness guardrails enabled to extract invoice fields and confidence scores
B) the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer
C) the Azure Content Understanding in Foundry Tools prebuilt-documentSearch analyzer and search.score from the Azure AI Search results for routing
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
2. Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure a prompt shield for documents.
Does this meet the goal?
A) No
B) Yes
3. You have a customer support agent that uses the Microsoft Foundry Agent Service.
Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following:
- Multi-turn continuity within the session
- Cross-session continuity for the same case
- Access to the full interaction history, including user messages,
agent messages, tool calls, and tool outputs
You need to ensure that the agent automatically reloads the complete history on each new turn.
What should you do?
A) Create and reuse a conversation by storing the conversation's ID and supplying the ID on subsequent requests.
B) Enable memory summarization on the agent definition to persist the context automatically.
C) Persist only the final model response stored in the client application and prepend the response to future prompts.
4. A production application authenticates to Microsoft Foundry using an API key stored in an environment variable. Your security team requires that you remove hardcoded secrets and enable per-principal auditing of every call. Which approach meets both requirements?
A) Share a single API key across services and restrict it with IP allow-listing
B) Move the API key into Azure Key Vault and rotate it monthly
C) Embed the API key in the deployment pipeline as a masked variable
D) Authenticate with Microsoft Entra ID using a managed identity (keyless)
5. You have a Microsoft Foundry project that contains an agent for a customer support chat app.
The agent uses a memory store and a memory search tool.
You need to ensure that the conversation history does NOT persist across separate sessions.
To what should you set the scope of the memory tool?
A) global
B) {{$conversationId}}
C) {{$userId}}
D) session
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: D |
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