QUESTION: 57

Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution.
Note: Each correct selection is worth one point.

dataset
compute
pipeline
module

Answer(s): A,D

Explanation:

You can drag-and-drop datasets and modules onto the canvas.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

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QUESTION: 58

You need to create a training dataset and validation dataset from an existing dataset.
Which module in the Azure Machine Learning designer should you use?

Select Columns in Dataset
Add Rows
Split Data
Join Data

Answer(s): C

Explanation:

A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data.
Use the Split Data module to divide a dataset into two distinct sets.
The studio currently supports training/validation data splits

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits

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QUESTION: 59

DRAG DROP (Drag and Drop is not supported)
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.
Select and Place:

See Explanation section for answer.

Answer(s): A

Explanation:

Box 1: Regression
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.

Box 2: Clustering
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.

Box 3: Classification
Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.