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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 2: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 3: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 4: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 5: Implement agentic solutions | 20-25% | - Build AI agents
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
You have a Microsoft Foundry project that generates short promotional product videos.
After several clips are approved, reviewers notice a small watermark in the top-right corner of some videos.
You need to remove the watermark without regenerating the videos.
What should you do?
- A. Apply a mask-based inpainting edit to the affected part of the video.
- B. Increase the guidance scale.
- C. Crop the video by using the size parameter.
- D. Modify the original prompt to exclude watermarks.
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
Hotspot Question
You plan to create a Microsoft Foundry project named Project1 that will contain an agent and use an Azure key vault named KV1.
You need to configure a connection from Project1 to KV1.
How should you complete the Bicep code? To answer, select the appropriate options in the answer area?
NOTE: Each correct selection is worth one point.
Explanation:
Box 1: AzureKeyVault
To configure a connection from a Microsoft Foundry project to an Azure Key Vault, you must use the AzureKeyVault connection category.
Box 2: AccountManagedIdentity
To connect a Microsoft Foundry project to an Azure Key Vault, you must use AccountManagedIdentity as the authType.
Microsoft Foundry secures its outbound connections to core Azure storage and security infrastructure using Microsoft Entra ID role-based access control (RBAC). Using AccountManagedIdentity completely eliminates the need to handle or rotate explicit credentials or api keys within your configuration.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/set-up-key-vault-connection
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. Persist only the final model response stored in the client application and prepend the response to future prompts.
- B. Enable memory summarization on the agent definition to persist the context automatically.
- C. Create and reuse a conversation by storing the conversation's ID and supplying the ID on subsequent requests.
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
You have an Azure subscription that contains an Azure Al Foundry instance named AI1.
You have an app that automatically triages and resolves issues presented in the log files of a system.
You create an incident manager agent and a DevOps agent that collaborate to resolve the issues.
You need to ensure that the incident manager agent can assign work to the DevOps agent. The solution must minimize development effort.
What should you do?
- A. Configure prompt flow for AI1.
- B. Create a plugin for the DevOps agent.
- C. Configure a connected agent for AI1.
- D. Create a plugin for the incident manager agent.
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.
The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:
- Support turn-taking dynamics, where the agent begins to generate the
speech output before the user finishes speaking.
- Operate with low latency to maintain conversational experience.
You need to enable both speech to text and text to speech in a real-time agent interaction.
What should you do?
- A. Use speech translation to convert the audio into another language and return the translated text.
- B. Use an embeddings model to encode the audio, and then decode the audio into text and speech.
- C. Use real-time speech to text for incoming audio and text to speech for agent responses.
- D. Use batch transcription to convert the audio input and return text responses from the agent.
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
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