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NEW QUESTION # 112
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: Yes
Yes, generative AI models like large language models operate precisely by predicting the next token in a sequence using statistical patterns learned during training How Next-Token Prediction Works Tokenization: Input text splits into small chunks called tokens, which can be whole words, parts of words, or characters.
Context Processing: The model reads the existing sequence of tokens to evaluate the surrounding context.
Probability Distribution: It calculates statistical likelihoods for every possible next token in its vocabulary.
Iterative Loop: A single token gets chosen and added to the text, then the model repeats the entire process to generate the next one.
Box 2: Yes
Yes, a system prompt is used to influence the behavior, tone, and boundaries of a generative AI model. It acts as a foundational, high-level instruction set provided before user inputs are processed.
Box 3: No
Increasing the temperature makes generative AI responses more random, creative, and varied, while lowering the temperature makes them more deterministic, predictable, and consistent.
Low Temperature (near 0): The model picks the most likely words every time. This creates consistent and factual answers.
High Temperature (closer to 1 or higher): The model takes more risks and picks less common words. This creates unique and surprising answers, but can cause mistakes or off-topic text.
Reference:
https://www.ucf.edu/artificial-intelligence/how-do-generative-ai-tools-like-chatgpt-work/
NEW QUESTION # 113
You need to deploy a generative AI model to a Microsoft Foundry project. Which factor should you use to identify which type of model can be deployed to the project?
Answer: D
Explanation:
Yes, Azure region availability determines whether you can use a Global, Data Zone, or Regional/Standard deployment type for your generative AI model.
Deployment Types and Region Dependencies
Global Deployments (Global Standard / Global Provisioned): Use these when there are no strict single-region data residency constraints; traffic and inferencing may route to any Azure region where the model is actively supported and has capacity.
Data Zone Deployments (Data Zone Standard / Data Zone Provisioned): Use these when data processing must be restricted to a specific compliance boundary (such as the EU or US data zone), requiring your project to reside in a region belonging to that designated zone.
Regional Deployments (Standard / Regional Provisioned): Use these when your workload requires data processing to stay strictly within the single Azure region associated with your project.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/reference/region-support
NEW QUESTION # 114
You are deploying a generative AI model to a Microsoft Foundry project.
You assign a higher tokens per minute (TPM) allocation to the model.
What is the result of this change?
Answer: C
Explanation:
A higher Tokens Per Minute (TPM) allocation to a generative AI model in a Microsoft Foundry project increases the project's maximum throughput capacity, allowing it to process more text per minute, handle concurrent users or larger prompts without throttling, and scale production workloads.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/quotas-limits
NEW QUESTION # 115
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The completed sentence is:
After deploying a vision-enabled GPT model in Microsoft Foundry, you can configure an application to send requests to the endpoint of the model .
To call a deployed model from an application, the application must send requests to the deployed model's endpoint and include the required authentication credentials. The Foundry playground is used for testing interactively, but application code calls the deployed model endpoint.
The other options are incorrect:
evaluation pipeline of model is used to evaluate model performance, not to receive inference requests from an app.
Foundry playground is an interactive testing environment, not the production target for application requests.
training dataset of the model is used for training or fine-tuning, not for sending inference requests.
Therefore, the correct answer is endpoint of the model .
NEW QUESTION # 116
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement
Answer
Image analysis capabilities in Microsoft Foundry Tools can perform optical character recognition (OCR).
Yes
Image analysis capabilities in Microsoft Foundry Tools can generate captions and descriptive tags for images.
Yes
Image analysis capabilities in Microsoft Foundry Tools are designed primarily to create new images from text prompts.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is Yes, Yes, No .
The first statement is Yes . Azure Vision Image Analysis in Microsoft Foundry Tools supports optical character recognition (OCR) . Microsoft documents Image Analysis 4.0 as supporting synchronous OCR through its Read capability, which extracts printed or handwritten text contained within images.
The second statement is also Yes . Image Analysis provides both captioning and image tagging . Captions generate a human-readable description of image content, while tags identify recognizable objects, scenes, actions, and other visual concepts. Microsoft explicitly lists capabilities such as Captions, Dense Captions, Tags, Object Detection, People detection, and Read/OCR as Image Analysis features.
The third statement is No . Image Analysis is primarily concerned with extracting information from existing images , not generating new images. Creating an image from a textual prompt is a generative AI/image- generation workload, typically performed by image-generation models. Image Analysis instead interprets visual input by extracting text, captions, tags, objects, and related information.
NEW QUESTION # 117
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