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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Identify common computer vision tasks - Describe Azure capabilities for computer vision |
| Topic 2: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Identify common machine learning tasks |
| Topic 3: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 4: Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |
| Topic 5: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify common NLP tasks - Identify Azure AI services for NLP |
You can change the time and type of questions of the Microsoft AI-900 exam dumps. Microsoft Azure AI Fundamentals practice questions improve your confidence and ability to complete the exam timely. The Microsoft AI-900 real questions are an advanced strategy to prepare you according to the test service. The Microsoft AI-900 Practice Exam software keeps track of previous attempts and shows the changes in each attempt. Knowing your weaknesses and overcoming them before the Microsoft AI-900 exam is easy.
NEW QUESTION # 78
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
NEW QUESTION # 79
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:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/get-started-build-detector
NEW QUESTION # 80
You need to predict the income range of a given customer by using the following dataset.
Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.
NEW QUESTION # 81
You need to identify harmful content in a generative Al solution that uses Azure OpenAI Service.
What should you use?
Answer: C
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Azure OpenAI documentation, the appropriate service for detecting and managing harmful, unsafe, or inappropriate content in text, images, or other generative AI outputs is Azure AI Content Safety.
Azure AI Content Safety is designed to automatically detect potentially harmful material such as hate speech, violence, self-harm, sexual content, or profanity. It ensures that generative AI applications like chatbots, image generators, and content creation tools comply with Microsoft's Responsible AI principles - specifically Reliability & Safety and Accountability.
This service integrates directly with the Azure OpenAI Service, meaning that when developers build AI solutions using models like GPT-4 or DALL E, they can use Content Safety to filter and moderate both input prompts and model outputs. This protects users from unsafe or offensive content generation.
Let's analyze why the other options are incorrect:
* A. Face - The Face service detects and analyzes human faces in images or videos. It is unrelated to moderating harmful textual or generative content.
* B. Video Analysis - This service analyzes video streams to detect objects, actions, or events but not inappropriate or harmful text or imagery from AI models.
* C. Language - The Azure AI Language service focuses on text understanding tasks like sentiment analysis, entity recognition, and translation, not content safety filtering.
Therefore, per Microsoft Learn's official AI-900 guidance, when identifying or filtering harmful content in a generative AI solution built with Azure OpenAI, the correct and verified service to use is Azure AI Content Safety.
NEW QUESTION # 82
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
NEW QUESTION # 83
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