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NEW QUESTION # 125
Hotspot Question
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: No
No, not all AI models in Azure can analyze and generate images. Azure hosts a vast model catalog (such as through Foundry Models) containing thousands of specialized foundation models, where capabilities depend entirely on the specific model architecture.
Box 2: Yes
A multimodal generative AI model in Azure can absolutely interpret image inputs within a prompt.
These advanced systems process text and visual data simultaneously. This allows you to ask questions about an uploaded image directly in your text prompt.
Box 3: Yes
These advanced systems process text and visual data simultaneously. This allows you to ask questions about an uploaded image directly in your text prompt.
Reference:
https://learn.microsoft.com/en-us/azure/developer/ai/get-started-app-chat-vision
NEW QUESTION # 126
You plan to create an AI application that will use Azure OpenAI Service. The solution requires that a specific amount of throughput be allocated. Which type of deployment should you use?
Answer: D
Explanation:
When a specific amount of throughput is needed with Azure OpenAI Service, you should use Provisioned Throughput Units (PTUs) for your deployment. PTUs allow you to reserve a fixed amount of processing capacity for your models, ensuring consistent performance and avoiding potential throttling or rate limits.
In contrast, the "Standard" deployment option in Azure OpenAI Service uses a "pay-as-you-go" model, where you are billed for the amount of usage. While this can be suitable for applications with fluctuating usage patterns, it does not provide the same level of throughput control as PTUs.
In summary: When you need to allocate a specific amount of throughput with Azure OpenAI Service, choose the "Provisioned" deployment type, which allows you to use PTUs.
Reference:
https://github.com/MicrosoftDocs/azure-ai-docs/blob/main/articles/ai-services/openai/concepts/provisioned-throughput.md
NEW QUESTION # 127
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Azure Content Understanding in Foundry Tools uses transcription to convert speech to text from audio and video.
Transcription is the process of converting spoken audio into written text. In Azure Content Understanding, audio and video analyzers can use transcription to produce text transcripts from spoken content.
The other options are incorrect:
diarization identifies or separates speakers, such as "Speaker 1" and "Speaker 2." key phrase extraction identifies important phrases from text after text exists.
synthesis converts text into spoken audio, which is the opposite direction of speech-to-text.
NEW QUESTION # 128
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: analyzes digital images to detect objects
A computer vision model analyzes digital images and video frames to locate, identify, and classify specific items by drawing bounding boxes around them.
How It Works
Input: The system ingests a raw image or video stream.
Pre-processing: The model normalizes pixel data, adjusts contrast, and clears noise.
Feature Extraction: Deep learning architectures like Convolutional Neural Networks (CNNs) scan for lines, shapes, and textures.
Classification and Localization: The model categorizes what the object is and maps out exact coordinates.
Reference:
https://www.ibm.com/think/topics/computer-vision
NEW QUESTION # 129
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:
Statement 1: Fairness can be achieved by focusing solely on improving the overall accuracy of an AI model. = No Overall accuracy alone does not guarantee fairness. A model can have high overall accuracy while still producing biased or lower-quality outcomes for specific demographic groups. Fairness requires evaluating and reducing differences in model behavior across groups.
Statement 2: Evaluating AI system outputs to identify and reduce bias across demographic groups supports fairness. = Yes This statement is correct. The Microsoft responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias. Evaluating outputs across demographic groups helps identify and reduce bias.
Statement 3: Ensuring fairness for an AI system means that all users always receive the same output from the system. = No Fairness does not mean identical outputs for all users. It means the system should avoid unfair bias and provide equitable treatment. Different users may receive different outputs when differences are relevant and appropriate.
NEW QUESTION # 130
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