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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement AI solutions by using Microsoft Foundry | 55-60% | - Work with models in Microsoft Foundry
|
| Topic 2: Identify AI concepts and responsibilities | 40-45% | - Describe principles of responsible AI
|
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NEW QUESTION # 43
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is API endpoint . After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.
An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference.
A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.
This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.
NEW QUESTION # 44
Select the answer that correct completes the sentence.
Answer:
Explanation:
Explanation:
Azure Text Analytics client library
The completed sentence is:
To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.
Azure Language text analysis features include sentiment analysis, key phrase extraction, named entity recognition, language detection, and other text analytics capabilities. These are accessed by using the Azure Text Analytics client library .
The highlighted Azure Speech SDK option is incorrect because it is used for speech workloads, such as speech recognition and speech synthesis, not text analysis.
NEW QUESTION # 45
You have the following REST API request.
Which Azure OpenAI model should you use to process the request?
Answer: B
Explanation:
A DALL-E REST API example using parameters like prompt, size, style, and n (number of images) can be used to generate images. Here's how:
Example Request Body (JSON):
{
"prompt": "A cat wearing a hat, standing on a skateboard",
"size": "1024x1024", // Supported values: "1024x1024", "1792x1024", "1024x1792"
"style": "natural", // DALL-E 3 supports "natural" and "vivid" styles
"n": 1 // DALL-E 3 only supports n=1
}
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/dall-e
NEW QUESTION # 46
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, limiting access to AI systems and data directly reduces the risk of unauthorized data exposure by minimizing attack surfaces and preventing accidental leaks.
Key Security Benefits
Fewer Entry Points: Restricting who and what can connect to your AI model lowers the chance of a security breach.
Controlled Data Flow: Limiting data inputs ensures sensitive user or corporate information does not enter training pipelines or public logs.
Better Compliance: Strict access rules help meet privacy laws and internal security policies.
Box 2: No
Sharing personal user data does not automatically improve collaboration or support responsible AI practices; in many cases, it creates significant privacy, security, and ethical risks that run counter to responsible AI principles.
Box 3: Yes
Yes, protecting personal data and complying with regulations are essential parts of responsible AI. They build trust, respect user privacy, and prevent illegal data use.
User Trust: People want to know their private data is safe.
Legal Rules: Laws punish companies that misuse data.
Fairness: Good data habits stop AI from learning bad or biased habits.
Reference:
https://www.linkedin.com/top-content/artificial-intelligence/ai-in-cybersecurity/data-security-issues-in-artificial-intelligence/
https://pmc.ncbi.nlm.nih.gov/articles/PMC10498316/
NEW QUESTION # 47
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 # 48
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