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| Section | Weight | Objectives |
|---|
| Topic 1: Implement AI solutions by using Microsoft Foundry | 55-60% | - Implement AI solutions
- 1. Monitor and manage AI solutions
- 2. Implement single-agent solutions
- 3. Use code examples to call AI services
- 4. Integrate AI services into applications
- Work with models in Microsoft Foundry
- 1. Fine-tune models
- 2. Discover and deploy foundation models
- 3. Customize models with prompt engineering
- 4. Evaluate model performance and safety
- Get started with Microsoft Foundry
- 1. Create and configure a Foundry project
- 2. Navigate the Microsoft Foundry portal
- 3. Describe core features and use cases of Microsoft Foundry
|
| Topic 2: Identify AI concepts and responsibilities | 40-45% | - Identify AI model components and configurations
- 1. Describe model training and evaluation concepts
- 2. Describe data preparation for AI models
- 3. Describe model deployment and consumption concepts
- Identify AI concepts and features
- 1. Describe capabilities of natural language processing
- 2. Describe characteristics of generative AI
- 3. Describe capabilities of computer vision
- 4. Describe types of AI workloads
- 5. Describe capabilities of machine learning
- Describe principles of responsible AI
- 1. Describe transparency considerations
- 2. Describe inclusiveness considerations
- 3. Describe privacy and security considerations
- 4. Describe reliability and safety considerations
- 5. Describe fairness considerations
- 6. Describe accountability considerations
|
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Microsoft Azure AI Fundamentals Sample Questions (Q23-Q28):
NEW QUESTION # 23
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You are developing an application that sends images to the model.
You need to ensure that the model can analyze the images.
In which two formats can you provide the images? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. a publicly accessible URL of the image
- B. a UTF-8 encoded text description of the image
- C. a JSON document that describes the image content
- D. a base64-encoded image data string
Answer: A,D
Explanation:
Images can be sent to these models either by passing a publicly accessible image URL or by sending Base64-encoded image data directly in the message payload.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/foundry-models/how-to/use-chat-multi-modal
NEW QUESTION # 24
You are using the Azure Speech SDK to develop a Python application that supports real-time spoken conversations. Which Azure speech class should you use to configure the connection to the Azure Speech service?
- A. SpeechSynthesizer
- B. AudioConfig
- C. AuditOutputConfig
Answer: C
NEW QUESTION # 25
For each of the following statement1, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:

Statement 1: Voice Live returns only transcribed text. = No
Voice Live is not limited to returning transcribed text. It is designed for real-time spoken conversational experiences and can include speech input, model reasoning, and spoken output.
Statement 2: Voice Live requires you to separately implement speech to text and text to speech services.
= No
Voice Live combines the conversational voice pipeline, so you do not need to separately build independent speech-to-text and text-to-speech services for the same real-time voice experience.
Statement 3: Voice Live combines speech to text, reasoning, and text to speech into a single conversational experience. = Yes This statement is correct. Voice Live is used for real-time conversational AI experiences where spoken input is processed, the model reasons over the request, and spoken output can be returned.
NEW QUESTION # 26
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 # 27
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Statement
Answer
Image analysis capabilities in Microsoft Foundry Tools can perform optical character recognition (OCR).
Yes
Image analysis capabilities in Microsoft Foundry Tools can generate captions and descriptive tags for images.
Yes
Image analysis capabilities in Microsoft Foundry Tools are designed primarily to create new images from text prompts.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is Yes, Yes, No.
The first statement is Yes. Azure Vision Image Analysis in Microsoft Foundry Tools supports optical character recognition (OCR). Microsoft documents Image Analysis 4.0 as supporting synchronous OCR through its Read capability, which extracts printed or handwritten text contained within images.
The second statement is also Yes. Image Analysis provides both captioning and image tagging. Captions generate a human-readable description of image content, while tags identify recognizable objects, scenes, actions, and other visual concepts. Microsoft explicitly lists capabilities such as Captions, Dense Captions, Tags, Object Detection, People detection, and Read/OCR as Image Analysis features.
The third statement is No. Image Analysis is primarily concerned with extracting information from existing images, not generating new images. Creating an image from a textual prompt is a generative AI/image- generation workload, typically performed by image-generation models. Image Analysis instead interprets visual input by extracting text, captions, tags, objects, and related information.
NEW QUESTION # 28
......
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