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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 2: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 4: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 5: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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NEW QUESTION # 141
You have an Azure Speech in Foundry Tools resource that hosts a custom speech to text model deployed to a custom endpoint. An agent uses the endpoint to perform real-time speech recognition.
You are approaching the expiration date of the custom speech to text model.
What is the expected behavior when the model expires?
Answer: A
Explanation:
When the custom speech-to-text model expires, the real-time endpoint will automatically fall back to using the most recent base model for that locale.
Because of this automated fallback design, your agent's real-time speech recognition streams will not fail or throw a connection error. However, you will likely experience a drop in transcription accuracy, as the fallback base model lacks the domain-specific vocabulary, acronyms, or unique audio adaptations built into your custom model.
Immediate Impact Summary
Real-Time Endpoints: Continue to process requests. The endpoint swaps the expired custom model for the newest standard base model behind the scenes.
Batch Transcriptions (If used): Any batch transcription jobs explicitly targeting the expired custom model ID will fail with a 4xx error code.
Customization Loss: Specific jargon, formatting rules, or accents trained into your model will temporarily stop applying to incoming agent audio.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/how-to-custom-speech-model-and-endpoint-lifecycle
NEW QUESTION # 142
You have an Azure subscription that contains an Azure App Service app named App1.
You provision a Microsoft Foundry Service resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?
Answer: D
Explanation:
To configure the App Service app to access the Microsoft Foundry Service (part of Azure AI Services) with low administration, you need to configure the app with the endpoint URI and a subscription key.
These credentials can be found in the Azure Portal under the Keys & Endpoint section of your provisioned Foundry resource, allowing the app to seamlessly authenticate API calls.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/authentication
NEW QUESTION # 143
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
Answer: D
NEW QUESTION # 144
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure image moderation to block unsafe content before processing the images.
Does this meet the goal?
Answer: B
Explanation:
Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.
Reference:
https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection
NEW QUESTION # 145
You have a Semantic Kernel agent that provides answers to product questions.
You plan to extend the capabilities of the agent to answer questions about the status of orders.
You need to ensure that the agent answers order status questions as quickly as possible. The solution must minimize development effort.
What should you include in the solution?
Answer: C
Explanation:
To enable the agent to answer questions about order status, you need a plugin.
In Semantic Kernel, a plugin acts as a container for the specific capabilities (functions) that allow an agent to interact with external data or systems, such as an order database or API.
To help me give you a more specific example or guide, could you tell me:
The data source where order info is kept (e.g., a SQL database, a REST API, or Shopify) The programming language you're using for the Kernel (C# or Python) Reference:
https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-functions
NEW QUESTION # 146
......
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