You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression a regression model.

You must predict the fare of a taxi trip.

You need to select performance metrics to correctly evaluate the- regression model.

Which two metrics can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

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You are evaluating a completed binary classification machine learning model.

You need to use the precision as the valuation metric.

Which visualization should you use?

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You create a classification model with a dataset that contains 100 samples with Class A and 10,000 samples with Class B

The variation of Class B is very high.

You need to resolve imbalances.

Which method should you use?

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HOTSPOT

You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment.

You need to configure the module.

You must meet the following requirements:

✑ Divide the data into subsets

✑ Assign the rows into folds using a round-robin method

✑ Allow rows in the dataset to be reused

How should you configure the module? 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:

Use the Split data into partitions option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

✑ Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.

✑ For Partition or sample mode, select Assign to Folds.

✑ Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.

✑ If you do not use replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold.

✑ Randomized split: Select this option if you want rows to be randomly assigned to folds.

If you do not select this option, rows are assigned to folds using the round-robin method.

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

HOTSPOT

You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.

You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:

✑ The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.

✑ Batch size must be 16, 32 and 64.

✑ Keep probability must be a value selected from a uniform distribution between the range of 0.05 and 0.1.

You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.

How should you complete the code segment? 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:

In random sampling, hyperparameter values are randomly selected from the defined search space.

Random sampling allows the search space to include both discrete and continuous hyperparameters.

Example:

from azureml.train.hyperdrive import RandomParameterSampling

param_sampling = RandomParameterSampling( {

"learning_rate": normal(10, 3),

"keep_probability": uniform(0.05, 0.1),

"batch_size": choice(16, 32, 64)

}

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters

You are a data scientist building a deep convolutional neural network (CNN) for image classification.

The CNN model you built shows signs of overfitting.

You need to reduce overfitting and converge the model to an optimal fit.

Which two actions should you perform? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

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You are with a time series dataset in Azure Machine Learning Studio.

You need to split your dataset into training and testing subsets by using the Split Data module.

Which splitting mode should you use?

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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 first 1.000 rows represent class 1 (10 percent).

The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using 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:

You are performing clustering by using the K-means algorithm.

You need to define the possible termination conditions.

Which three conditions can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

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You are building a regression model tot estimating the number of calls during an event.

You need to determine whether the feature values achieve the conditions to build a Poisson regression model.

Which two conditions must the feature set contain? I ach correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

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