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| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Model selection and lifecycle management - Responsible AI principles and governance |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
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NEW QUESTION # 32
You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase.
You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?
Answer: C
Explanation:
The correct capability is token usage . In Microsoft Foundry observability, token consumption is the primary signal for diagnosing model-cost changes when request volume is unchanged. Token usage lets you distinguish whether costs increased because prompts became larger, retrieved or tool-provided context expanded, responses became longer, or agent execution added more model calls. Microsoft Foundry monitoring dashboards track operational metrics such as token consumption, latency, error rates, and quality scores, and the agent monitoring dashboard is specifically intended to help analyze token usage, latency, success rates, and evaluation outcomes for production traffic.
This directly matches the scenario because the issue is not more traffic, but changed request or response characteristics. Input tokens reveal whether the prompt, chat history, grounding data, or tool outputs being sent to the model increased. Output tokens reveal whether the model is generating longer completions.
Expanded tool usage can also increase cost indirectly by adding more tool results, intermediate calls, and context into subsequent model requests; Foundry tracing and observability capture tool usage and token consumption for agent runs.
Evaluation metrics assess response quality and safety, not cost drivers. Latency identifies performance delays, and run success rate measures reliability. Reference topics: Microsoft Foundry observability, agent monitoring dashboard, token consumption, cost analysis, tool usage, and production monitoring.
NEW QUESTION # 33
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?
Answer: C
NEW QUESTION # 34
You need to ensure that the marketing department can generate videos by using the model deployed to Project2. now should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Call client.videos.create() to submit the prompt and start a video-generation job against the model deployment. Video generation is asynchronous: the initial response represents a job and contains properties such as its unique ID and current status rather than immediately returning the completed video. Microsoft's official Python example creates the job by using client.videos.create(model=..., prompt=...).
The application must then poll the job. After waiting between requests, client.videos.retrieve(video.id) obtains the latest state of that specific job. Polling continues until the status becomes completed, failed, or cancelled.
list would enumerate jobs rather than refresh the selected job, while download_content is used only after successful completion to retrieve the generated video file.
To comply with Contoso's prohibition on API keys, the client should use a Microsoft Entra bearer-token provider. The OpenAI Python client accepts this provider through its api_key constructor parameter even though no static API key is used.
Study Guide alignment: design and implement video-generation solutions and integrate generative workflows through Foundry SDKs.
NEW QUESTION # 35
You have an Azure subscription that has the following configurations:
* Subscription ID: 8d3591aa-96b8-4737-ad09-00f9bled35ad
* Tenant ID: 3edfe572-cb54-3ced-ael2-c5cl77f39al2
You plan to create a resource that will perform sentiment analysis and optical character recognition (OCR).
You need to use an HTTP request to create the resource in the subscription. The solution must use a single key and endpoint How should you complete the request? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Azure Resource Manager resource paths are scoped through the subscription , not the Microsoft Entra tenant.
Therefore, the URL must contain:
subscriptions/8d3591aa-96b8-4737-ad09-00f9b1ed35ad
The tenant ID identifies the Microsoft Entra directory but does not replace the subscription portion of an ARM resource path.
The appropriate resource provider is Microsoft.CognitiveServices . Azure AI service accounts, including multi-service Azure AI resources, are provisioned under the Microsoft.CognitiveServices/accounts resource type.
A multi-service resource is required because the solution needs both Language sentiment analysis and Vision OCR while exposing a consolidated Azure resource with common key/endpoint management. Microsoft.
ApiManagement manages API gateways, Microsoft.ContainerService manages Kubernetes services, and Microsoft.KeyVault manages keys and secrets rather than provisioning Azure AI capabilities.
A typical ARM resource path therefore contains:
/subscriptions/ < subscription-id > /resourceGroups/ < rg > /providers/Microsoft.CognitiveServices/accounts/
< name >
This aligns with the AI-103 requirement to design Azure infrastructure for AI apps and choose appropriate Foundry/Azure AI services . Microsoft Learn
NEW QUESTION # 36
Hotspot Question
You develop a test method to verify the results retrieved from a call to the Azure Vision in Foundry Tools API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
The code segment correctly filters for and displays the name (and coordinates) of each detected brand only if the model's confidence score is 75 percent or higher.The expression if brand.confidence >= 0.75 guarantees that only brands meeting or exceeding this threshold are printed.
Box 2: Yes
The code segment will display the coordinates. Specifically, it prints the x and y values of the rectangle's top-left corner alongside its width (w) and height (h) for any detected brand with a confidence score equal to or greater than 0.75 (75%).
The provided code uses the properties directly to extract the bounding box:
brand.rectangle.x and brand.rectangle.y: The coordinates of the top-left corner of the bounding box.
brand.rectangle.w and brand.rectangle.h: The width and height of the bounding box.
Box 3: No
See Box 2 above.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-brand-detection
NEW QUESTION # 37
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