You must store data in Azure Blob Storage to support Azure Machine Learning.

You need to transfer the data into Azure Blob Storage.

What are three possible ways to achieve the goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

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You are moving a large dataset from Azure Machine Learning Studio to a Weka environment.

You need to format the data for the Weka environment.

Which module should you use?

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You plan to deliver a hands-on workshop to several students. The workshop will focus on creating data visualizations using Python. Each student will use a device that has internet access.

Student devices are not configured for Python development. Students do not have administrator access to install software on their devices. Azure subscriptions are not available for students. You need to ensure that students can run Python-based data visualization code.

Which Azure tool should you use?

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Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are analyzing a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

You need to analyze a full dataset to include all values.

Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.

Does the solution meet the goal?

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Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are a data scientist using Azure Machine Learning Studio.

You need to normalize values to produce an output column into bins to predict a target column.

Solution: Apply a Quantiles binning mode with a PQuantile normalization.

Does the solution meet the goal?

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HOTSPOT

You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).

The remaining 1,000 rows represent class 1 (10 percent).

The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.

You need to configure the module.

Which values should you use? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: 300

You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).

Box 2: 5

We should use 5 data rows.

Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.

By increasing the number of nearest neighbors, you get features from more cases.

By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

You are solving a classification task.

You must evaluate your model on a limited data sample by using k-fold cross validation. You start by

configuring a k parameter as the number of splits.

You need to configure the k parameter for the cross-validation.

Which value should you use?

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DRAG DROP

You are creating an experiment by using Azure Machine Learning Studio.

You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data. You must prepare the data for analysis.

You need to select appropriate methods for producing the experiment.

Which three modules should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

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Correct Answer:

Explanation:

The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.

Incorrect Answers:

Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation

(LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.

Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.

Missing Value Scrubber: The Missing Values Scrubber module is deprecated.

Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.

Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

HOTSPOT

You are retrieving data from a large datastore by using Azure Machine Learning Studio.

You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.

You add the Partition and Sample module to your experiment.

You need to select the properties for the module.

Which values should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: Sampling

Create a sample of data

This option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

HOTSPOT

You are retrieving data from a large datastore by using Azure Machine Learning Studio.

You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.

You add the Partition and Sample module to your experiment.

You need to select the properties for the module.

Which values should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: Sampling

Create a sample of data

This option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.