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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
You plan to build an app that will generate a list of tags for uploaded images. The app must:
* Generate tags in a user ' s preferred language.
* Support English, French, and Spanish.
* Minimize development effort.
Which Azure service endpoint should you use?
- A. Azure Custom Vision image classification
- B. Content Safety in Foundry Control Plane
- C. Azure Vision in Foundry Tools Image Analysis
- D. Custom Translator
Correct Answer: C 🗳️
Explanation: Only visible for PassReview members. You can sign-up / login (it's free).
You have an app that uses Azure Al Custom Vision and a custom trained classifier to identify products in images. You need to add new products to the classifier. The solution must meet the following requirements:
* Minimize how long it takes to add the products.
* Minimize development effort.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
Because the existing classifier is an Azure AI Custom Vision project, the lowest-effort workflow is performed directly in the Custom Vision portal , not Azure Machine Learning studio or Vision Studio. Open the existing project so that the new product classes can be incorporated into its current training dataset.
Next, upload representative sample images of each new product. The images should include sufficient variation in viewing angle, lighting, background, size, and presentation to support reliable classification.
Label each image with the appropriate new product tag. Custom Vision uses these tags as the target classes during supervised training. Microsoft's portal workflow explicitly supports uploading and manually tagging images without requiring application code.
Retrain the classifier after the labeled images have been added. Training creates a new iteration based on all current training images and tags, including the newly introduced product categories.
Finally, publish the trained iteration to the prediction resource. Publishing makes that iteration available through the Custom Vision Prediction API used by the application.
Study Guide alignment: create, train, evaluate, retrain, publish, and consume custom image-classification models .
You have a Microsoft Foundry project that contains an agent.
You need to enable long-term memory to ensure that the agent can recall user preferences across separate conversations. Stored memories must be isolated per authenticated user without the client application manually generating user IDs.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
scope = " {{userId}} "
tools = [memory_tool]
The correct scope value is {{userId}} because the requirement is per-authenticated-user memory isolation without the client application manually generating user identifiers. In Microsoft Foundry Agent Service memory, the scope parameter partitions memory items inside the memory store. The official guidance states that when the memory search tool is attached to an agent, setting scope to the user identity template enables per-user memory isolation; the service resolves the end-user identity from the request header when provided, or falls back to the Microsoft Entra tenant ID and object ID of the caller. This matches the requirement to isolate stored preferences by authenticated user automatically.
The tools property must be [memory_tool] because the MemorySearchTool instance is created earlier and must be attached to the PromptAgentDefinition. Foundry guidance shows the memory search tool being passed in the agent definition as tools=[tool] , allowing the agent to read from and write to the configured memory store during conversations.
" session " and {{conversationId}} would limit continuity to a session or conversation instead of enabling long-term recall across separate conversations. [mem_store_name] is a list containing the store name, not a tool definition. Reference topics: Foundry Agent Service memory, memory stores, memory search tools, scope, and per-user isolation.
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 a 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 real-time speech to text for incoming audio and text to speech for agent responses.
- B. Use an embeddings model to encode the audio, and then decode the audio into text and speech.
- C. Use speech translation to convert the audio into another language and return the translated text.
- D. Use batch transcription to convert the audio input and return text responses from the agent.
Correct Answer: A 🗳️
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You need a connection method for a GPT realtime model that supports live client-side audio streaming and approximately 100-ms latency.
- A. WebRTC
- B. SIP
- C. WebSocket
- D. RTMP
Correct Answer: A 🗳️
Explanation: Only visible for PassReview members. You can sign-up / login (it's free).
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