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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of computer vision workloads on Azure | 15-20% | - Image analysis
|
| Topic 2: Describe Artificial Intelligence workloads and considerations | 20-25% | - Responsible AI principles
|
| Topic 3: Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Text analysis
|
| Topic 4: Describe features of generative AI workloads on Azure | 10-15% | - Azure OpenAI and generative services
|
| Topic 5: Describe fundamental principles of machine learning on Azure | 25-30% | - Machine learning concepts
|
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NEW QUESTION # 35
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 36
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 1: In the new Microsoft Foundry portal, you must fine-tune a model before you can deploy the model. = No Fine-tuning is optional. Microsoft's Foundry model deployment documentation describes deploying Foundry Models directly from the model catalog for inference. It does not require fine-tuning first.
Statement 2: In the new Microsoft Foundry portal, you can test a model from the model catalog only after you deploy the model. = No Microsoft documentation states that some Foundry Tools are available to try via the model catalog without a project , and Foundry playgrounds are used for prototyping and validation before production.
Therefore, the statement using "only after you deploy" is too restrictive.
Statement 3: In the new Microsoft Foundry portal, you can deploy a model from the model catalog only after retraining the model. = No Retraining/fine-tuning is not required before deployment. Microsoft states that after you deploy a Foundry Model, you can interact with it in the Foundry Playground and use it from code, and the deployment workflow starts by selecting a model from the model catalog and choosing Deploy .
NEW QUESTION # 37
Which action can be performed by using the Azure Vision in Foundry Tools service?
Answer: B
Explanation:
Azure Vision in Foundry Tools supports OCR capabilities for reading printed and handwritten text from images and documents. Microsoft documentation states that OCR extracts text from images and documents, including handwritten text.
Therefore, extracting data from handwritten letters is the action that can be performed by using Azure Vision in Foundry Tools.
NEW QUESTION # 38
You have a Microsoft Foundry project that contains a vision-enabled chat model deployment.
You are developing a Python application that uses the responses API. The application sends a request that includes a user prompt and a local JPEG image.
You need to include the local image in the request.
Which value should you use for the image input?
Answer: D
Explanation:
For a local JPEG image in a Python application using the Azure OpenAI Responses API, the image should be read, base64 encoded, and sent as a data URL, such as:
{ " type " : " input_image " , " image_url " : " data:image/jpeg;base64, < base64_image_data > " } Microsoft's Responses API documentation shows vision-enabled requests using input_image with image_url set to a base64 data URL in the format data:image/jpeg;base64,{base64_image}. It also confirms that JPEG images are supported.
A local file path such as file:///C:/images/photo.jpg or C:\images\photo.jpg is not a valid API-accessible image input. A public or accessible HTTPS URL can be used, but the question specifically says the application sends a local JPEG image , so the correct choice is the base64 data URL.
NEW QUESTION # 39
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: D
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 # 40
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