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| Section | Objectives |
|---|---|
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Model selection and lifecycle management - Responsible AI principles and governance |
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
The Prep4SureReview is a leading platform that has been helping the Developing AI Apps and Agents on Azure (AI-103) exam candidates in exam preparation and boosting their confidence to pass the final AI-103 exam. The Prep4SureReview is offering real, valid, and updated Developing AI Apps and Agents on Azure (AI-103) practice questions. These Developing AI Apps and Agents on Azure (AI-103) exam questions are verified by Microsoft AI-103 exam trainers. They work closely and check all Developing AI Apps and Agents on Azure (AI-103) exam dumps one by one and they ensure the best possible answers to Developing AI Apps and Agents on Azure (AI-103) exam dumps.
NEW QUESTION # 72
You need a connection method for a GPT realtime model that supports live client-side audio streaming and approximately 100-ms latency.
Answer: C
Explanation:
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
NEW QUESTION # 73
You have a Microsoft Foundry project that contains an agent for a customer support chat app.
The agent uses a memory store and a memory search tool.
You need to ensure that the conversation history does NOT persist across separate sessions.
To what should you set the scope of the memory tool?
Answer: D
Explanation:
To ensure that conversation history does not persist across separate sessions, you must set the scope to session.
The session scope restricts data access to the current active chat instance. It automatically wipes or ignores previous data when a new session starts.
Incorrect:
[Not C]
user or {{$userId}} scope: Persists data across multiple sessions for that specific user. This would cause the exact issue you want to avoid by carrying historical context into new conversations.
References:
https://ai.gopubby.com/agents-with-memory-conceptual-undestanding-part-01-f6caedfcd96d?gi=a9aa95df15c6
NEW QUESTION # 74
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.
Answer:
Explanation:
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
NEW QUESTION # 75
You have a Microsoft Foundry project that contains a customer support agent built on a deployed chat model.
The agent responses are validated by using an automated testing system that compares generated answers to stored expected outputs. Identical prompts must return consistent response to prevent automated test failures.
You need to reduce response variability, without modifying the prompt or reducing factual accuracy.
What should you do for the model?
Answer: D
Explanation:
To reduce response variability and ensure identical prompts return consistent answers, you should decrease the temperature parameter.
Temperature controls the randomness of the model's output. Setting the temperature closer to 0 makes the model deterministic. It forces the model to choose the highest-probability words every time, ensuring that identical prompts consistently yield identical or near-identical responses to pass your automated testing.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/runtime-components
NEW QUESTION # 76
A custom NER model uses one ContactInfo entity for phone numbers, email addresses, and social-media handles. Precision is low.
What should you do before retraining?
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The existing ContactInfo entity combines several distinct entity patterns into one heterogeneous label. Phone numbers, email addresses, and social-media handles have substantially different syntax, contextual clues, and boundary patterns.
Splitting the broad entity into Phone , Email , and SocialMedia gives the custom NER model a clearer and more internally consistent training signal. Every existing tagged span should then be relabeled using the correct specialized entity class before retraining.
This can improve precision because the model no longer needs to learn one extremely broad decision boundary that covers unrelated forms of contact data. More precise labels also help the model learn the correct start and end boundaries of each entity, addressing the reported false positives in which adjacent text is included.
Adding more examples under the same ambiguous ContactInfo schema may reinforce the underlying modeling ambiguity. Auto-labeling reduces annotation effort but does not correct the schema problem.
Lowering a confidence threshold would generally cause more uncertain predictions to be accepted, tending to lower rather than improve precision.
Study Guide alignment: implement solutions that extract entities and customize language-model outputs for domain-specific extraction tasks . Microsoft Learn
NEW QUESTION # 77
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