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NEW QUESTION # 86
What should you use to compare benchmark metrics of different AI models?
Answer: C
Explanation:
To compare benchmark metrics across Azure AI models, navigate to the Azure AI Foundry portal, explore the model catalog, and use the benchmarking tools to assess model performance. You can then compare these metrics to choose the most suitable model for your needs.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/benchmark-model-in-catalog
NEW QUESTION # 87
You are explaining generative AI workflows to a colleague. What is the first step of a generative AI workflow?
Answer: C
Explanation:
Training a generative AI model using data is a foundational step in the workflow, but it is preceded by data collection and preparation. After the model is trained, subsequent steps involve validation, generation, and refinement.
Steps in a generative AI workflow
1. Data Gathering: Collect a massive, relevant dataset (images, text, audio, etc.) that will be used for training.
2. Data Preparation: Clean and preprocess the raw data. This includes removing errors, standardizing formats, and structuring the data so the model can comprehend it.
*-> 3. Model Training: Feed the prepared data into the model. During this stage, the model learns patterns and relationships within the data to understand how to create new content.
4. Model Validation: Test the model on a separate set of data to evaluate its performance and accuracy.
5. Content Generation: Use the trained and validated model to create new content based on prompts.
6. Refinement: Fine-tune the model using human feedback or additional data to improve its output and address specific tasks.
Reference:
https://www.tricension.com/domainai/proper-training-for-generative-ai/
NEW QUESTION # 88
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You use the Azure OpenAI Responses API to send a prompt to the model.
You need to provide an image for analysis.
Which content item should you include in the request?
Answer: A
Explanation:
When using the Azure OpenAI Responses API with a vision-enabled model, the image must be included as an input image content item. The correct content item type is:
{"type": "input_image", "image_url": image_url}
Microsoft's Azure OpenAI Responses API documentation states that the Responses API supports image inputs, and multimodal requests use structured input content items for the request.
Why the other options are incorrect:
A . image_base64 = Incorrect. Base64 data can be used as the image data format, but the content item type is still input_image.
B . image_generation = Incorrect. This is related to generating images, not providing an image for analysis.
C . output_image = Incorrect. This would refer to generated output, not image input.
D . input_image = Correct.
NEW QUESTION # 89
Hotspot Question
Select the answer that correctly complete the sentence.
Answer:
Explanation:
NEW QUESTION # 90
What are two types of generative Al models? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Answer: C,E
Explanation:
GPT and DALL-E are examples of generative AI model types/families.
GPT models generate natural language text, such as answers, summaries, emails, and conversations.
DALL-E models generate images from text descriptions.
Why the other options are incorrect:
A). transformer is a model architecture, not the best answer here as a generative model type/family.
B). computer vision is an AI workload area.
E). natural language processing is an AI workload area, not a specific generative AI model type.
NEW QUESTION # 91
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