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The Microsoft AI-901 certification is on trending nowadays, and many Microsoft aspirants are trying to get it. Success in the Microsoft Azure AI Fundamentals (AI-901) test helps you land well-paying jobs. Additionally, the Microsoft AI-901 certification exam is also beneficial to get promotions in your current company. But the main problem that every applicant faces while preparing for the AI-901 Certification test is not finding updated Microsoft AI-901 practice questions.
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>> AI-901 Hot Spot Questions <<
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NEW QUESTION # 77
Drag and Drop Question
You have a Microsoft Foundry project that contains a model deployment.
You are developing an application that sends an image and a user question to the model.
You need to send both text and image content in the same request so the model can return an answer.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 78
Match the principles of responsible Al to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The system must not discriminate based on gender, race, or age. = Fairness This requirement maps to fairness because fairness means AI systems should avoid unfair bias and treat people equitably across demographic groups.
Personal data must be visible only to approved users. = Privacy and security This requirement maps to privacy and security because it focuses on protecting personal data and restricting access to authorized users only.
Automated decision-making processes must be recorded so that approved users can identify why a decision was made. = Transparency This requirement maps to transparency because recording decision-making processes helps users understand and explain how or why an AI-supported decision was made.
NEW QUESTION # 79
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 # 80
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
A schema defines which fields to extract when analyzing content.
In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.
The other options are incorrect:
A keyword list does not define the complete structured output fields.
OCR-only processing extracts text, but it does not define structured fields.
A synchronous API call describes a request pattern, not the extraction schema.
NEW QUESTION # 81
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:
NEW QUESTION # 82
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