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Microsoft AI-900 exam, also known as the Microsoft Azure AI Fundamentals certification exam, is a great way to demonstrate your proficiency in artificial intelligence (AI) and its applications in the cloud. AI-900 exam is designed for individuals who are interested in pursuing a career in AI or want to showcase their knowledge in this field. AI-900 exam covers a wide range of topics related to AI, machine learning, and natural language processing.
Microsoft AI-900 Exam is an entry-level certification exam that does not require any prerequisites. However, candidates should have a basic understanding of cloud computing and Microsoft Azure. AI-900 exam consists of 40-60 multiple-choice questions, and candidates have 60 minutes to complete it. Upon passing the exam, candidates will receive the Microsoft Certified: Azure AI Fundamentals certification, which is a valuable credential in the AI industry.
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Microsoft AI-900 Exam (Microsoft Azure AI Fundamentals) is a certification exam that tests your foundational knowledge of artificial intelligence and machine learning on the Azure cloud platform. AI-900 exam is designed for individuals who want to build a career in AI or for professionals who want to incorporate AI into their current job roles. Microsoft Azure AI Fundamentals certification is ideal for data scientists, software developers, business analysts, and IT professionals who want to gain a better understanding of AI concepts and how they can be applied to solve business problems.
NEW QUESTION # 209
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 210
You have the following apps:
* App1: Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment
* App2: Uses images from cameras to track individual livestock as they move around a farm
* App3: Identifies brands in photographs of billboards
What does each app use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Let's analyze each application in the context of Microsoft Azure AI Fundamentals (AI-900) and computer vision model types.
* App1 - Uses a set of images of tumors to identify whether the tumors are benign or malignant and suggest a treatment # Image classificationThis application is performing image classification, where each image (of a tumor) is assigned to a single predefined category - benign or malignant. Image classification models learn patterns from labeled training images and predict the correct class for new ones. In this case, the model identifies the type of tumor, a classic binary classification scenario.
* App2 - Uses images from cameras to track individual livestock as they move around a farm # Object detectionThis scenario describes object detection, which not only identifies what objects (in this case, animals) are in an image but also locates them by drawing bounding boxes. Tracking movement requires detecting the position of each animal frame by frame. Object detection models are well-suited for use cases involving counting, tracking, or monitoring objects in a visual scene.
* App3 - Identifies brands in photographs of billboards # Optical character recognition (OCR)This app involves reading and interpreting text (brand names, slogans, or logos) from images of billboards.
Optical Character Recognition (OCR), part of Azure AI Vision, extracts textual information from images or scanned documents. Once extracted, that text can be analyzed to identify brand names or keywords.
Summary:
* App1 # Image classification
* App2 # Object detection
* App3 # Optical character recognition (OCR)
NEW QUESTION # 211
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 212
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
NEW QUESTION # 213
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning," a regression model is used when the goal is to predict a continuous numerical value based on historical data.
In this question, the task is to predict the sale price of auctioned items, which is a numeric output that can take on a wide range of values (for example, $50.25, $199.99, etc.). This makes it a regression problem because the output is continuous rather than categorical.
Regression models analyze the relationship between input features (such as item type, condition, age, bidding history, or demand) and a numerical target variable (the sale price). Common regression algorithms include linear regression, decision tree regression, and neural network regression. In Azure Machine Learning, these models are trained using labeled datasets containing known outcomes to learn patterns and make future predictions.
Let's review the incorrect options:
* Classification: Used to predict discrete categories or labels, such as "sold" vs. "unsold" or "low,"
"medium," "high." It cannot output continuous numeric predictions.
* Clustering: An unsupervised technique used to group similar data points based on shared characteristics, not to predict specific numeric outcomes.
Therefore, because predicting a sale price involves forecasting a continuous numerical value, the correct model type is Regression.
This aligns with Microsoft's AI-900 teaching that regression is used for tasks such as:
* Predicting house prices
* Forecasting sales revenue
* Estimating car values or auction prices
NEW QUESTION # 214
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