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Dumps AI-900 Vce - Free AI-900 Practice Exams

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The Microsoft AI-900 Exam consists of multiple-choice questions, and individuals have to score a minimum of 700 points out of 1000 to pass the exam. AI-900 exam can be taken online, and individuals can prepare for the exam by taking online courses or attending training sessions. Microsoft offers various training resources, including online courses, practice tests, and instructor-led training, to help individuals prepare for the exam.

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Microsoft AI-900, also known as the Microsoft Azure AI Fundamentals certification exam, is designed for individuals who want to learn about the fundamental concepts of Artificial Intelligence (AI) and how they can be implemented on Microsoft's Azure platform. Microsoft Azure AI Fundamentals certification is an excellent starting point for those who are new to AI and want to understand the basics of AI and its uses in real-world scenarios.

Microsoft Azure AI Fundamentals Sample Questions (Q71-Q76):

NEW QUESTION # 71
Match the principles of responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:

Explanation:

NEW QUESTION # 72
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

NEW QUESTION # 73
correctly completes the sentence.

Answer:

Explanation:

Explanation:
Features
The correct completion of the sentence is:
"In a machine learning model, the data that is used as inputs are called features." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Identify features of common machine learning types," the term feature refers to an individual measurable property or characteristic of the data that is used by a machine learning model to make predictions or decisions.
In supervised and unsupervised learning, features serve as the inputs to the model. They are the variables that represent the information the algorithm learns from. For example, if a model predicts the price of a house, the features might include square footage, number of bedrooms, location, and age of the property. These features feed into the model so that it can learn the relationship between inputs and outputs.
Microsoft Learn further defines these key concepts:
* Features: Input variables (independent variables) used by the model to learn patterns.
* Labels: The desired output or target variable that the model is trained to predict (e.g., price, category).
* Instances: Individual rows or data records within the dataset (each instance has multiple features).
* Functions: Algorithms or mathematical operations used during training and prediction - not data inputs.
Therefore, among the provided options - features, functions, labels, instances - only features accurately describe the data elements used as inputs for training or inference in a machine learning model.
In summary, within the AI-900 learning context:
* Features = inputs to the model.
* Labels = outputs for supervised learning.
* Instances = examples (rows) of data.

NEW QUESTION # 74
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://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

NEW QUESTION # 75
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

NEW QUESTION # 76
......

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