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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
| Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
You need to add an automated grounding check to a RAG application's continuous evaluation.
The check must return a simple pass or fail result and must not require you to deploy a separate judge model. Which evaluator should you use?
- A. Groundedness Pro
- B. Groundedness, which returns a 1 to 5 score
- C. Fluency
- D. Relevance
Correct Answer: A 🗳️
Explanation: Only visible for ExamsTorrent members. You can sign-up / login (it's free).
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 Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You increase the value of the max_tokensparameter.
Does this meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
Explanation: Only visible for ExamsTorrent members. You can sign-up / login (it's free).
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.
App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.
Some uploaded images contain embedded text.
You need to prevent potentially malicious instructions from being processed by the model.
What should you use?
- A. image moderation
- B. prompt shields for documents
- C. protected material text
- D. prompt shields for user prompts
Correct Answer: B 🗳️
Explanation: Only visible for ExamsTorrent members. You can sign-up / login (it's free).
Hotspot Question
You need to recommend a plan to create a customer support agent by using the Microsoft Foundry Agent Service. The agent must meet the following requirements:
- Retain user preferences across multiple conversations.
- Enable users to provide contextual grounding by directly uploading
documents during a chat.
Which Foundry capability should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Drag and Drop Question
You have a Microsoft Foundry project that contains a multi-agent solution. The agents use tool calling to query internal systems.
You need to implement responsible AI auditing to meet the following requirements:
- Capture all the nested operations across the entire agent run.
- Record tool invocation arguments and retuned results as metadata.
What should you use for each requirement? To answer, drag the appropriate options to the correct targets Each option may be used once, more than once, or not at all. You may need o dag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Hierarchical spans
Hierarchical spans should be used in this case.
Parent-Child tracking: In OpenTelemetry and Azure Monitor Application Insights (which back Microsoft Foundry environments), hierarchical spans utilize a unique Trace ID for the entire request and distinct Span IDs for individual operations.
Nested capture: When a primary agent calls a sub-agent, or an agent invokes a specific system tool, each subsequent operation is recorded as a child span. This explicitly preserves the parent- child relationship, allowing auditors to reconstruct the exact execution tree of all nested operations across the entire multi-agent run Box 2: Tool call attributes To record tool invocation arguments and returned results as metadata for responsible AI auditing in a Microsoft Foundry multi-agent project, you should use Tool call attributes.
In GenAI and multi-agent systems leveraging OpenTelemetry semantic conventions (which Microsoft Foundry utilizes for its observability, tracing, and logging pillars), tool execution details are captured via specific span attributes. Configured tool call attributes explicitly map and record the parameters (tool.call.arguments) and outputs (tool.call.result) as metadata keys inside the tracing context for auditing and validation.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/observability/concepts/trace-data
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/ai-observability-starter-kit-for-microsoft-foundry-agents/4522751
