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Microsoft Azure AI Fundamentals Sample Questions (Q149-Q154):

NEW QUESTION # 149
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 150
For a machine learning progress, how should you split data for training and evaluation?

Answer: A

Explanation:
In Azure Machine Learning, the percentage split is the available technique to split the data. In this technique, random data of a given percentage will be split to train and test data.
Reference:
https://www.sqlshack.com/prediction-in-azure-machine-learning/


NEW QUESTION # 151
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
Text Description automatically generated


NEW QUESTION # 152
Match the machine learning models to the appropriate deceptions.
To answer, drag the appropriate model from the column on the left to its description on the right Each model may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.

Answer:

Explanation:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the three main machine learning model types differ by their purpose and the kind of data they use - whether supervised (using labeled data) or unsupervised (using unlabeled data).
* Regression # A supervised machine learning model used to predict numeric values.Regression is a type of supervised learning that predicts continuous numerical outcomes. It learns the relationship between input features (independent variables) and a continuous target variable (dependent variable). Examples include predicting house prices, sales revenue, or temperature. The AI-900 curriculum emphasizes regression for "predicting numeric values based on known data," using algorithms such as linear regression or decision tree regression.
* Classification # A supervised machine learning model used to predict categories.Classification is also a supervised learning technique, but it predicts discrete outcomes (classes) instead of continuous values.
It assigns input data to one or more categories based on learned patterns. Typical examples include spam detection (spam vs. not spam), sentiment analysis (positive, neutral, negative), or predicting loan approval (approved/denied). The AI-900 study materials describe classification as "predicting a category or label for new observations."
* Clustering # An unsupervised machine learning model used to group similar entities based on features.
Clustering is an unsupervised learning approach, meaning it works on unlabeled data. It automatically identifies patterns and groups similar data points into clusters based on shared characteristics. Examples include customer segmentation (grouping customers by behavior) and grouping similar documents. The AI-900 learning module explains clustering as "discovering natural groupings in data without predefined labels." Thus, per Microsoft's official AI-900 learning objectives:
* Regression # Predicts numeric/continuous values.
* Classification # Predicts categories/labels.
* Clustering # Groups similar entities (unsupervised).
# Final Verified Configuration:
* Regression # Predict numeric values
* Classification # Predict categories
* Clustering # Group similar entities based on features


NEW QUESTION # 153
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.

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


NEW QUESTION # 154
......

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