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Microsoft AI-900 Exam, also known as the Microsoft Azure AI Fundamentals exam, is a certification exam that tests individuals on their knowledge of artificial intelligence and its applications in Microsoft Azure. AI-900 exam is designed for individuals who are interested in learning about AI and its practical use in the cloud, and for those who are looking to gain a basic understanding of AI terminology, concepts and technologies.
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The technologies that are tested on Microsoft AI-900 exam are necessary to develop intelligent operations based on cloud computing. Audience for this certification exam is individuals who work as computer programmers, developers, system administrators and project managers. Built for high-volume, high-speed web applications that use Azure, this Examines the skills required to develop intelligent cloud solutions. Smarter IT environments are the goal of this exam. Single sign-on for Azure, is an exam objective. Microsoft AI-900 exam dumps helps to make it easier for students to get this certification. Outline the business value of the exam. The importance of having this certification. Identify the technologies covered on Microsoft AI-900 exam. Gain a better understanding of the skills you'll need to develop intelligent cloud solutions.
Incredible opportunities are associated with this certification. Background on Azure and its products, services, networks, and the scalability of its computing infrastructure. Background of cloud computing in general. Automatically configure Azure virtual machines. Implement and configure SQL Server Integration Services (SSIS), Visual Studio Team Services (VSTS) and other Microsoft technologies. Predict the effects of each of the possible outcomes of the solution design. Led you to the final test. Ability to perform automated deployment of Azure services.
Workers who take this exam should have experience in the IT industry and should understand the architecture and business requirements of Azure. Leave no stone unturned. Personalized study plan for self-paced learning. Tests your knowledge in the whole Cloud Computing path from Azure to Office 365.
NEW QUESTION # 222
You are building an AI system.
Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?
Answer: C
Explanation:
Section: Describe Artificial Intelligence workloads and considerations
Explanation/Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
NEW QUESTION # 223
You have a dataset that contains the columns shown in the following table.
You have a machine learning model that predicts the value of ColumnE based on the other numeric columns.
Which type of model is this?
Answer: A
Explanation:
The dataset described contains numeric columns (ColumnA through ColumnE). The model's task is to predict the value of ColumnE based on the other numeric columns (A-D). This is a classic regression problem.
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn module
"Identify common types of machine learning," a regression model is used when the target variable (the value to predict) is continuous and numeric, such as price, temperature, or-in this case-a numerical value in ColumnE.
Regression models analyze relationships between independent variables (inputs: Columns A-D) and a dependent variable (output: ColumnE) to predict a continuous outcome. Common regression algorithms include linear regression, decision tree regression, and neural network regression.
Option analysis:
* A. Regression: # Correct. Used for predicting numerical, continuous values.
* B. Analysis: # Incorrect. "Analysis" is a general term, not a machine learning model type.
* C. Clustering: # Incorrect. Clustering is unsupervised learning, grouping similar data points, not predicting values.
Therefore, the type of machine learning model used to predict ColumnE (a numeric value) from other numeric columns is Regression, which fits perfectly within Azure's supervised learning models.
NEW QUESTION # 224
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/evaluate-model
NEW QUESTION # 225
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 # 226
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language- processing
NEW QUESTION # 227
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