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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure (15-20%) | 15-20% | - Describe core machine learning concepts
|
| Topic 2: Describe features of generative AI workloads on Azure (20-25%) | 20-25% | - Identify generative AI services and capabilities in Microsoft Azure
|
| Topic 3: Describe features of computer vision workloads on Azure (15-20%) | 15-20% | - Identify Azure tools and services for computer vision tasks
|
| Topic 4: Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) | 15-20% | - Identify features of common NLP workload scenarios
|
| Topic 5: Describe Artificial Intelligence workloads and considerations (15-20%) | 15-20% | - Identify features of common AI workloads
|
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NEW QUESTION # 20
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is Creating captions for a video recording . Speech recognition, also called speech-to-text , converts spoken audio into textual output. Microsoft explicitly defines Azure Speech-to-Text as speech recognition technology that transcribes audio streams or prerecorded audio into text.
Caption generation is a direct application of this capability. Microsoft describes captioning as converting the audio content of a video, film, webcast, or other production into text and displaying that text visually.
Therefore, creating captions from the spoken content of a video is a canonical speech-recognition workload.
Creating an audio commentary is instead associated with speech synthesis or text-to-speech because the required output is spoken audio. Identifying key phrases in a video transcript is a text-analysis/NLP task performed after the speech has already been transcribed. A voice-activated security system may involve voice authentication, speaker recognition, or command recognition, but it is not as unambiguously a speech-to-text workload as caption generation.
The AI-901 Study Guide specifically requires candidates to distinguish the features and capabilities of speech recognition and speech synthesis .
NEW QUESTION # 21
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct selection is AIProjectClient . In the Microsoft Foundry SDK, AIProjectClient serves as the primary project-level client for accessing and managing resources associated with a Foundry project.
Microsoft documentation specifically exposes operation groups through this client for deployments , agents , and indexes , which directly matches the requirements stated in the question.
For example, the SDK exposes deployments operations for working with models deployed to the project, agents operations for creating and managing agents, and indexes operations for accessing project search indexes. Microsoft also describes the Azure AI Projects client library as a unified Foundry SDK component that connects applications to project resources through a single project endpoint.
ChatCompletionsClient is focused primarily on model inference and chat-completion operations rather than comprehensive project-resource management. FoundryLocalManager is associated with local Foundry capabilities, while ModelCatalogClient would relate to model discovery/catalog functionality rather than deployments, agents, and indexes collectively.
Therefore, the class required for this scenario is AIProjectClient .
NEW QUESTION # 22
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Counting the number of animals in an area based on a video feed is an example of computer vision.
Computer vision is the AI workload used to analyze visual content such as images and video. Counting animals in a video feed requires detecting visual objects in video frames and analyzing them, which is a computer vision task.
Forecasting predicts future values or trends from historical data.
Knowledge mining extracts insights from large document or data collections.
Therefore, the correct answer is computer vision.
NEW QUESTION # 23
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: C
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 # 24
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The completed sentence is:
Ensuring that human reviewers oversee AI-generated decisions and remain responsible for the final output is an example of the Microsoft responsible AI principle of accountability .
Accountability means people and organizations remain responsible for AI systems and their effects. Human review and responsibility for final decisions are examples of accountability.
The other options are incorrect:
fairness focuses on avoiding bias and treating people fairly.
privacy and security focuses on protecting data and restricting access.
transparency focuses on explaining AI use, capabilities, and limitations.
NEW QUESTION # 25
......
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