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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
| Topic 2: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe machine learning pipelines - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning |
| Topic 5: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Describe capabilities of Azure Language |
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NEW QUESTION # 37
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:
NEW QUESTION # 38
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
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.
Box 3: No
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION # 39
You need to identify street names based on street signs in photographs.
Which type of computer vision should you use?
Answer: D
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of computer vision workloads on Azure", Optical Character Recognition (OCR) is a core computer vision workload that enables AI systems to detect and extract text from images or scanned documents.
In this scenario, the goal is to identify street names from street signs in photographs. Since the text is embedded within images, OCR is the correct technology to use. OCR works by analyzing the visual patterns of letters, numbers, and symbols, then converting them into machine-readable text. Azure's Computer Vision API and Azure AI Vision Service provide OCR capabilities that can extract printed or handwritten text from pictures, documents, and even real-time camera feeds.
Let's analyze the other options:
* A. Object detection: Identifies and locates objects (like cars, people, or street signs) but not the text written on them.
* C. Image classification: Classifies an entire image into categories (e.g., "street scene" or "traffic sign") but doesn't extract text content.
* D. Facial recognition: Identifies or verifies people by analyzing facial features, unrelated to text extraction.
Therefore, identifying street names on street signs is a text extraction problem, making Optical Character Recognition (OCR) the most accurate and verified answer per Microsoft Learn content.
NEW QUESTION # 40
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
According to the AI-900 official study guide and Microsoft Learn module "Describe features of conversational AI workloads on Azure", conversational AI refers to artificial intelligence systems that interact with users through natural language via text or speech. These systems include chatbots, virtual assistants, and interactive voice response (IVR) systems that simulate human conversation.
* B. Chatbot that provides users with the ability to find answers on a website by themselvesThis is a classic example of conversational AI. Chatbots use natural language understanding (LUIS) and Azure Bot Service to interpret user input, identify intent, and provide relevant responses automatically. They help users self-serve information without human support, such as retrieving account details or answering FAQs.
* C. Telephone voice menus to reduce the load on human resourcesAutomated telephone systems or IVRs use conversational AI to interpret spoken commands and route calls intelligently. This is often implemented using Azure Cognitive Services Speech (for speech-to-text and text-to-speech) combined with Azure Bot Service for managing dialogue flow.
NEW QUESTION # 41
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application, email Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
NEW QUESTION # 42
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