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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe automated machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning |
| Topic 3: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Topic 4: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision |
Taking practice tests is particularly helpful for those who have exam anxiety. Our practice tests are user-friendly and customizable. Windows support the desktop practice test software. Our web-based practice test is compatible with all browsers and operating systems. The web-based Microsoft Azure AI Fundamentals (AI-900) practice test is similar to the desktop-based exam and can be taken on any browser without needing to download separate software.
NEW QUESTION # 241
You are developing a conversational AI solution that will communicate with users through multiple channels including email, Microsoft Teams, and webchat.
Which service should you use?
Answer: C
Explanation:
According to the Microsoft Azure AI Fundamentals official study guide and Microsoft Learn module
"Describe features of conversational AI workloads on Azure", Azure Bot Service is the core Azure platform for building, testing, deploying, and managing conversational agents or chatbots. These bots can communicate with users across multiple channels, including email, Microsoft Teams, Slack, Facebook Messenger, and webchat.
Azure Bot Service integrates deeply with the Bot Framework SDK and Azure Cognitive Services such as Language Understanding (LUIS) or Azure AI Language, enabling natural language processing and multi- channel message delivery. The service abstracts away channel management, meaning that developers can build one bot logic that connects seamlessly to several communication platforms.
Option analysis:
* A. Text Analytics is a Cognitive Service used for text mining tasks like key phrase extraction, language detection, and sentiment analysis - not for building chatbots.
* C. Translator provides language translation but cannot manage conversations or multi-channel delivery.
* D. Form Recognizer extracts structured information from documents and forms - unrelated to conversational interaction.
The AI-900 course explicitly defines Azure Bot Service as "a managed platform that enables intelligent, multi- channel conversational experiences between users and bots." This service allows businesses to unify chat experiences across multiple digital communication channels.
Thus, based on the official Microsoft Learn content and AI-900 syllabus, the best and verified answer is B.
Azure Bot Service, as it is the designated Azure solution for deploying a single conversational AI experience accessible from multiple platforms such as email, Teams, and webchat.
NEW QUESTION # 242
You have the Predicted vs. True chart shown in the following exhibit.
Which type of model is the chart used to evaluate?
Answer: C
Explanation:
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m
NEW QUESTION # 243
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 244
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:
This question is based on identifying Natural Language Processing (NLP) workloads, which is a fundamental topic in the Microsoft Azure AI Fundamentals (AI-900) certification. According to the official Microsoft Learn module "Describe features of natural language processing (NLP) workloads on Azure", NLP enables computers to understand, interpret, and generate human language - both written and spoken.
* A bot that responds to queries by internal users - YesThis is an example of a natural language processing workload because it involves understanding and generating human language. A chatbot interprets user input (queries written or spoken) using language understanding and text analytics, and then produces appropriate responses. On Azure, this can be implemented using Azure AI Language (LUIS) and the Azure Bot Service, both core NLP technologies.
* A mobile application that displays images relating to an entered search term - NoThis application involves searching for or displaying images, which falls under the computer vision workload, not NLP.
Computer vision focuses on analyzing and interpreting visual data like photos or videos, while NLP deals with language and text processing.
* A web form used to submit a request to reset a password - NoA password reset form involves structured input fields and user authentication, not natural language understanding or generation. It's part of standard web development and identity management, not an NLP-related process.
Therefore, based on Microsoft's AI-900 curriculum definitions:
# The only true NLP example is the bot responding to user queries, since it processes and understands natural language input to generate conversational output.
NEW QUESTION # 245
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn's modules on Computer Vision, object detection is the AI technique used to identify and locate multiple objects within an image. Unlike simple image classification, which only labels an entire image with a single category (for example, "This is a product"), object detection not only identifies the type of object but also pinpoints its exact position by providing bounding boxes and coordinates within the image.
In the scenario described - identifying the location of products on a conveyor belt - the system must be able to detect multiple items simultaneously and determine their spatial positions. Object detection algorithms (such as YOLO, Faster R-CNN, or SSD) are specifically designed for this purpose. This allows automation systems, like robotic arms or quality inspection systems, to track product locations in real time for sorting, packaging, or defect detection.
Let's evaluate the other options:
* Image classification only determines what is in the image, not where it is located. It cannot handle multiple objects or their positions.
* Image processing involves operations like resizing, filtering, or adjusting contrast, not understanding object placement.
* Optical character recognition (OCR) extracts text from images and documents, unrelated to locating physical items.
Thus, per Microsoft Learn's AI-900 guidance, object detection is the correct computer vision capability when a task requires both identification and spatial localization of items in an image or video stream.
# Final answer: Object detection
NEW QUESTION # 246
......
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