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Microsoft AI-103 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 2: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
| Topic 3: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Topic 4: Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
| Topic 5: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
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 building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.
You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?
- A. Run a batch transcription job on recorded audio files.
- B. Use speech translation to generate the transcripts into multiple languages.
- C. Use real-time speech to text to process streaming audio input.
- D. Use text to speech by using a custom neural voice.
Correct Answer: C π³οΈ
Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).
You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAl v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?
- A. Cognitive Services User
- B. Cognitive Services Data Reader
- C. Cognitive Services OpenAl User
- D. Contributor
Correct Answer: C π³οΈ
Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).
You have a Microsoft Foundry project that contains a customer support agent. The agent calls an internal knowledge API tool before generating responses.
Users report the following issues:
* Some requests take more than 15 seconds to complete.
* Some responses are incorrect, even when the knowledge API returns the expected data.
You need to inspect individual agent runs to view the ordered sequence of large language model (LLM) calls, tool invocations, and timing information.
Which observability capability should you use?
- A. safety metrics
- B. monitoring
- C. tracing
- D. token usage
Correct Answer: C π³οΈ
Explanation: Only visible for BootcampPDF members. You can sign-up / login (it's free).
You have a Microsoft Foundry project that uses Azure Content Understanding in Foundry Tools to analyze marketing videos.
Video segmentation is enabled.
You need to configure an analyzer to output a generated JSON field that describes the color scheme of each video segment.
How should you configure the analyzer? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
* Field method: generate
* Field value type: string
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The field should use the generate method because "describe the color scheme" asks Content Understanding to infer visual characteristics from each segment and generate a new semantic description. The desired value is not simply literal text that can be extracted from the media.
The field value type is string because a color-scheme description is textual-for example, " warm red and orange palette " or " predominantly cool blue and gray tones " .
Microsoft ' s Content Understanding video documentation explains that after segmentation, the generative- model layer can interpret scenes and populate developer-defined fields for every segment. Field schemas define a field ' s name, type, and description, and generated fields can capture semantic information derived from the source content. Microsoft Learn Therefore, the two values shown in the supplied answer image are reversed. The technically correct configuration is:
method = generate
type = string
This directly aligns with AI-103 ' s computer-vision objectives to implement visual understanding by configuring Azure Content Understanding to extract visual characteristics and process and interpret video segments . Microsoft Learn
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