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Microsoft AI-900, also known as Microsoft Azure AI Fundamentals, is a certification exam that aims to test an individual's knowledge of artificial intelligence (AI) concepts and how they can be applied to Microsoft Azure. AI-900 exam covers a range of topics, including machine learning, natural language processing, computer vision, and cognitive services. Microsoft Azure AI Fundamentals certification is designed for professionals who are looking to understand the basics of AI and how it can be used to solve business problems.
The Microsoft AI-900 Exam is ideal for individuals who are looking to kickstart their career in the field of AI or for professionals who want to enhance their knowledge of AI and its applications in Azure. AI-900 exam is also beneficial for students who are interested in learning about AI and its potential applications. Passing the AI-900 exam will not only validate one's understanding of AI and Azure but will also showcase their commitment to staying up-to-date with the latest technologies and trends in the industry.
NEW QUESTION # 137
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 138
Match the tasks to the appropriate machine learning models.
To answer, drag the appropriate model from the column on the left to its scenario on the right. Each model may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide, the three main types of supervised and unsupervised machine learning models-classification, clustering, and regression-are used for distinct problem types depending on the structure of the data and the prediction goal.
* Clustering is an unsupervised learning technique used when the goal is to group items with similar characteristics without predefined labels. In this scenario, "Assign categories to passengers based on demographic data" implies automatically grouping passengers based on patterns such as age, income, or travel frequency, without any prior labeling. This directly maps to clustering, which discovers hidden groupings (for example, segmenting customers into categories like business travelers or vacationers).
* Regression is a supervised learning method used to predict continuous numerical values. The scenario
"Predict the amount of consumed fuel based on flight distance" is a classic regression problem because the output (fuel consumption) is a continuous variable dependent on another continuous variable (distance). Regression models, such as linear regression, are trained to estimate numeric outputs.
* Classification is also a supervised learning approach, but it predicts discrete categories or outcomes.
The scenario "Predict whether a passenger will miss their flight based on demographic data" involves a binary decision (missed or not missed), which is typical of classification tasks. These models learn from labeled examples to assign new instances to specific categories.
In summary, Clustering groups similar passengers, Regression predicts continuous numerical outcomes, and Classification determines categorical outcomes. This alignment precisely matches the definitions in Microsoft' s AI-900 learning objectives under "Describe common machine learning types and scenarios."
NEW QUESTION # 139
You run a charity event that involves posting photos of people wearing sunglasses on Twitter.
You need to ensure that you only retweet photos that meet the following requirements:
Include one or more faces.
Contain at least one person wearing sunglasses.
What should you use to analyze the images?
Answer: C
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
NEW QUESTION # 140
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:
Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
NEW QUESTION # 141
Match the tool to the Azure Machine Learning task.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION # 142
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