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NEW QUESTION # 103
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
Answer
Generative AI models predict the next token in a sequence based on patterns learned during training.
Yes
A system prompt can be used to influence the behavior and tone of the responses generated by a generative AI model.
Yes
Increasing the Temperature parameter makes the responses generated by generative AI models more deterministic and consistent.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The first statement is Yes . Generative language models generate text autoregressively by estimating probabilities for the next token based on preceding context and patterns learned during model training. This next-token prediction mechanism is fundamental to how transformer-based generative models produce sequences of text.
The second statement is also Yes . A system prompt provides high-level instructions that establish how the model should behave. It can define role, response style, tone, constraints, and other behavioral expectations.
The AI-901 study guide explicitly includes creating effective system and user prompts as part of implementing generative AI applications in Microsoft Foundry.
The third statement is No . Increasing Temperature does the opposite of making output more deterministic.
Microsoft documents that higher temperature values increase randomness and output diversity, while lower values make responses more focused and predictable. At a temperature approaching zero, model selection becomes substantially more deterministic because higher-probability tokens are favored more strongly.
NEW QUESTION # 104
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?
Answer: B
Explanation:
The requirement is to extract structured fields, including nested values, from invoices by using a defined schema . In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.
Microsoft's Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms.
Microsoft's quickstart also shows invoice processing with the prebuilt-invoice analyzer to extract structured data from an invoice document.
Why the other options are incorrect:
A). transcription workflow in Azure Speech is for converting audio to text, not invoice field extraction.
B). OCR-only document processing can extract text but does not meet the requirement for structured fields and nested values by schema.
D). Azure AI Search is for indexing and querying content, not defining invoice extraction schemas.
Therefore, the correct answer is C. an analyzer in Azure Content Understanding in Foundry Tools .
NEW QUESTION # 105
You need to build an AI solution that responds to user queries with detailed written explanations.
Which type of AI model should you use?
Answer: B
Explanation:
A text generation model is the correct tool when you want an AI solution that responds to user queries with detailed written explanations.
Natural Language: They write clear sentences and paragraphs.
Complex Ideas: They break down hard topics into simple words.
Flexible Format: They can make lists, summaries, or long guides.
NEW QUESTION # 106
Your company processes customer support emails.
You need to implement an AI solution that automatically identifies mentions of people, organizations, and locations in the emails.
Which text analysis technique should you use?
Answer: B
Explanation:
The adequate text analysis feature is Named Entity Recognition (NER), which is a core prebuilt capability of the Azure AI Language service.
Prebuilt Categorization: The prebuilt NER feature automatically parses unstructured text (like emails) to identify and group elements into standard classes, including Person, Organization, and Location.
No Training Required: Because it relies on state-of-the-art pretrained transformer models, it can be deployed immediately out-of-the-box without requiring data labeling or custom model training.
High Extensibility: If your emails contain highly specialized terms (such as proprietary product IDs or industry-specific roles), you can easily scale to Custom NER within the same architecture.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/overview
NEW QUESTION # 107
You have the following REST API request.
Which Azure OpenAI model should you use to process the request?
Answer: D
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 # 108
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