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NEW QUESTION 156
Which two actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? (Each correct answer presents part of the solution. Choose two.)

A.    Calculate the accuracy of the model.
B.    Score test data by using the model.
C.    Combine multiple datasets.
D.    Use the model for real-time predictions.
E.    Remove records that have missing values.

Answer: CE
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-data-ingestion
https://docs.microsoft.com/en-us/azure/architecture/data-science-process/prepare-data

NEW QUESTION 157
You need to predict the animal population of an area. Which Azure Machine Learning type should you use?

A.    regression
B.    clustering
C.    classification

Answer: A
Explanation:
Regression is a supervised machine learning technique used to predict numeric values.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/1-introduction

NEW QUESTION 158
Which two languages can you use to write custom code for Azure Machine Learning designer? (Each correct answer presents a complete solution. Choose two.)

A.    Python
B.    R
C.    C#
D.    Scala

Answer: AB
Explanation:
Use Azure Machine Learning designer for customizing using Python and R code.
https://azure.microsoft.com/en-us/services/machine-learning/designer/#features

NEW QUESTION 159
When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable. This is an example of which Microsoft guiding principle for responsible AI?

A.    transparency
B.    inclusiveness
C.    fairness
D.    privacy and security

Answer: A
Explanation:
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai

NEW QUESTION 160
You are building a tool that will process images from retail stores and identify the products of competitors. The solution will use a custom model. Which Azure Cognitive Services service should you use?

A.    Custom Vision
B.    Form Recognizer
C.    Face
D.    Computer Vision

Answer: A
Explanation:
Azure Custom Vision is an image recognition service that lets you build, deploy, and improve your own image identifier models. An image identifier applies labels (which represent classifications or objects) to images, according to their detected visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify your own labels and train custom models to detect them.
https://learn.microsoft.com/en-us/answers/questions/947550/feature-engineering-methodes.html

NEW QUESTION 161
What are two metrics that you can use to evaluate a regression model? (Each correct answer presents a complete solution. Choose two.)

A.    coefficient of determination (R2)
B.    F1 score
C.    root mean squared error (RMSE)
D.    area under curve (AUC)
E.    balanced accuracy

Answer: AC
Explanation:
A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.
C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.
Incorrect:
Not B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model.
Not D: aucROC or area under the curve (AUC) is used to evaluate a classification model.
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics

NEW QUESTION 162
Which type of machine learning should you use to identify groups of people who have similar purchasing habits?

A.    classification
B.    regression
C.    clustering

Answer: C
Explanation:
Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset.
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

NEW QUESTION 163
HotSpot
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
AI-900-Exam-Dumps-1631

Answer:
AI-900-Exam-Dumps-1632
Explanation:
– Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset.
– Regression is a machine learning task that is used to predict the value of the label from a set of related features.
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

NEW QUESTION 164
HotSpot
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
AI-900-Exam-Dumps-1641

Answer:
AI-900-Exam-Dumps-1642
Explanation:
Box 1: No. The validation dataset is different from the test dataset that is held back from the training of the model.
Box 2: Yes. A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model’s hyperparameters.
Box 3: No. The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
https://machinelearningmastery.com/difference-test-validation-datasets/

NEW QUESTION 165
HotSpot
To complete the sentence, select the appropriate option in the answer area.
AI-900-Exam-Dumps-1651

Answer:
AI-900-Exam-Dumps-1652
Explanation:
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

NEW QUESTION 166
HotSpot
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:
AI-900-Exam-Dumps-1661
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
AI-900-Exam-Dumps-1662

Answer:
AI-900-Exam-Dumps-1663
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/filter-based-feature-selection

NEW QUESTION 167
Drag and Drop
You need to use Azure Machine Learning designer to build a model that will predict automobile prices. Which type of modules should you use to complete the model? (To answer, drag the appropriate modules to the correct locations. Each module 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.)
AI-900-Exam-Dumps-1671

Answer:
AI-900-Exam-Dumps-1672
Explanation:
Box 1: Select Columns in Dataset. For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.
Box 2: Split data. Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed.
Box 3: Linear regression. Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model.
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score

NEW QUESTION 168
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