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| Section | Objectives |
|---|---|
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
>> AI-103 Fragen Und Antworten <<
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21. Frage
You need a connection method for a GPT realtime model that supports live client-side audio streaming and approximately 100-ms latency.
Antwort: A
Begründung:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use WebRTC . Microsoft ' s current Realtime API documentation explicitly recommends WebRTC for low- latency real-time audio streaming and lists it as the preferred connection type for client-side applications such as web and mobile apps. The documented latency is approximately 100 ms . Microsoft Learn WebRTC is designed specifically for interactive media and provides capabilities appropriate for continuous microphone input and audio playback, including low-latency media transport and handling of real-time network conditions.
Microsoft distinguishes this from WebSocket , which is positioned primarily for server-to-server/backend scenarios and has a higher typical latency of approximately 200 ms in the current guidance. SIP is appropriate when integrating telephony infrastructure such as call centers or IVR systems. RTMP is not the supported GPT Realtime interactive client protocol for this scenario. Microsoft Learn Therefore, the answer shown in the supplied file ( A, WebSocket ) conflicts with current Microsoft Learn documentation. For a browser/client application targeting approximately 100-ms audio latency, the verified answer is C, WebRTC .
Study Guide alignment: build multimodal/realtime generative applications and integrate speech as an agent modality . Microsoft Learn
22. Frage
You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project. User messages can contain both text and images. Currently, App1 includes image URLs as plain text inside the message content, so the model cannot recognize them as images. You need to send the message as a structured array that includes both the text portion and the image reference.
Antwort: C
Begründung:
A vision-enabled Chat Completions request must represent the user message as a multimodal content array.
One item contains the prompt with " type " : " text " , while another contains the image reference with " type ": " image_url " and an image_url.url property:
" content " : [
{ " type " : " text " , " text " : " Analyze this image. " },
{ " type " : " image_url " , " image_url " : { " url " : " < image-url > " }}
]
This structure explicitly identifies each modality, allowing the deployed model to route the text and image through the appropriate processing paths. A URL embedded in an ordinary string remains text and is not interpreted as visual input. Microsoft's documented Chat Completions schema shows the text and image objects inside the user message's content array.
Request metadata does not define model input, and a system message should contain behavioral instructions rather than the user's image attachment. Base64 images are supported, but the encoded value must be formatted as a data URL and supplied through the structured image item-not inserted into a plain content string.
Study Guide alignment: deploy and consume multimodal models, integrate generative workflows into applications, and design multimodal-understanding workflows .
23. Frage
You are building an agent that will retrieve the current time at a given location by using a custom API. You need to test the functionality of the custom API.
How should you complete the command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
* Endpoint selection: assistants
* Function declaration property: tools=[
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The command is creating/configuring an assistant that has access to a custom function named get_current_time, so the REST resource shown in the exhibit is assistants .
For the function definition, current Azure OpenAI/Foundry function-calling syntax uses the tools collection.
A function tool is represented by an item with " type " : " function " and a nested " function " object containing its name, description, JSON parameter schema, and required arguments.
Microsoft explicitly documents that the older functions and function_call parameters have been deprecated and that tools and tool_choice should be used instead. Microsoft Learn Therefore, although the embedded exhibit ' s answer image shows functions=[, the currently verified Microsoft syntax is tools=[ .
content represents message content rather than an agent tool definition. tool_resources is used for resources required by certain hosted tools and is not where a custom function schema is declared.
The AI-103 Study Guide requires candidates to define tool schemas and integrate agent tools, including APIs and custom functions . Microsoft Learn
24. Frage
You plan to notify users that their video and text data has been processed by an AI-enabled sales system.
Which responsible AI principle does this help meet?
Antwort: B
Begründung:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The responsible-AI principle most directly addressed is transparency .
Transparency requires organizations to communicate appropriately about how AI systems operate and how AI is involved in experiences or decisions. Informing users that their uploaded or generated content has been processed by an AI-enabled sales system helps users understand that automated processing is occurring rather than leaving the role of AI undisclosed.
Fairness concerns whether the system treats different groups equitably and avoids unjustified bias.
Inclusiveness focuses on ensuring that technology accommodates and empowers users with diverse abilities, backgrounds, and needs. Reliability and safety focus on predictable operation, resilience, testing, failure handling, and avoidance of unsafe outcomes.
All of those principles can apply to the complete system, but user notification and disclosure are specifically transparency controls.
A production implementation may supplement the notification with explanations of what information is processed, the purpose of processing, important system limitations, relevant privacy notices, and escalation
/contact mechanisms.
The AI-103 Study Guide includes responsible-AI governance through safety controls, evaluations, auditing, provenance, and human oversight; transparency is a foundational principle informing those practices.
Microsoft Learn
25. Frage
You have a Microsoft Foundry project that contains an agent. The agent uses Azure AI Search for Retrieval Augmented Generation (RAG). You plan to ingest and index PDF product manuals. You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.
Antwort: C
Begründung:
Use vector search because it performs similarity matching based on semantic meaning rather than requiring literal keyword overlap. During ingestion, the product manuals should be divided into appropriately sized chunks. An embedding model converts each chunk into a numerical vector, which Azure AI Search stores in a vector field. At query time, the user's question is also converted into an embedding, and the service retrieves chunks whose vectors are closest to the query vector. Consequently, wording such as "reset the device to factory settings" can match manual content such as "restore the unit's original configuration," despite minimal lexical overlap. Azure AI Search explicitly defines vector fields as enabling similarity searches over mathematically represented content and supports this retrieval pattern for RAG applications.
Semantic ranking improves the ordering of an existing result set but is not the indexing mechanism that stores embeddings and performs nearest-neighbor similarity matching. Suggesters provide search-as-you-type functionality, while analyzers control tokenization and lexical processing for text fields.
This aligns with the AI-103 Study Guide objectives for implementing RAG, configuring Azure AI Search grounding, generating embeddings, and selecting vector retrieval techniques for agentic solutions.
26. Frage
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