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NEW QUESTION # 36
You are deploying a generative Al 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:
Tokens per minute, or TPM , is a throughput allocation for a model deployment. Assigning a higher TPM allocation increases the amount of token processing capacity available to the deployment, which affects the speed and scale at which the deployment can process requests.
It does not directly make responses longer, change region availability, or automatically add citations.
NEW QUESTION # 37
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When using the OpenAI Responses API and a vision-enabled model, you can include an image in a request by providing the image as a base64-encoded image data .
Azure OpenAI / Microsoft Foundry vision requests support image input as an input_image item. The image can be provided by URL or as base64-encoded image data in the request. Microsoft documentation for Azure OpenAI batch examples also lists Base64 encoded image and Image URL as supported input patterns for Responses API requests.
The other options are incorrect:
a CSV file attachment is not an image input format for vision analysis.
an MP4 video stream is video content, not an image format.
a shared access signature (SAS) token is only an access token for a protected resource; it is not the image data itself.
NEW QUESTION # 38
You have a Microsoft Foundry project that contains a generative AI model deployment.
You test the model by using the Foundry playground.
You need to develop an application that sends requests to the deployed model.
Which information must the application include to call the model?
Answer: B
Explanation:
To call a deployed Azure OpenAI model from an application, the app must know the service endpoint and authenticate its request. Microsoft documentation states that Azure OpenAI supports API key authentication or Microsoft Entra ID authentication, and API key authentication requires including the API key in the request. Microsoft quickstart guidance also states that to successfully make a call against Azure OpenAI, you need an endpoint and a key.
The application does not need the model training dataset, the Foundry project display name, or exported playground session history to call the deployed model.
NEW QUESTION # 39
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 # 40
You are developing an application that analyzes invoices by using Azure Content Understanding in Foundry Tools.
You have a custom analyzer named invoiceAnalyzer.
You need to use the analyzer to process invoice files.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The correct parameter is analyzer_id. The Azure AI Content Understanding Python SDK uses the ContentUnderstandingClient.begin_analyze() method to submit content for processing. Microsoft defines the method signature with analyzer_id as the required identifier that specifies which analyzer should process the supplied input.
Therefore, the code should be completed as:
poller = client.begin_analyze(analyzer_id= " invoiceAnalyzer " , ...)
A custom analyzer encapsulates the configuration that determines how Content Understanding interprets the source content and which fields or structured information it extracts. Because the analyzer has already been created with the name invoiceAnalyzer, that value is supplied through analyzer_id when the analysis operation begins.
extraction_type is not the parameter used to select an analyzer. model_name would refer conceptually to an underlying AI model, but Content Understanding analysis is invoked through an analyzer abstraction rather than by specifying a model directly. Likewise, schema_id does not identify the analyzer to execute.
Microsoft ' s custom-analyzer guidance explicitly demonstrates client.begin_analyze (analyzer_id=analyzer_id, inputs=[...]), confirming the required SDK pattern
NEW QUESTION # 41
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