DP-100 Designing and Implementing a Data Science Solution on Azure - Practice Questions

Question 10 (Mixed Questions)

You are building a binary classification model by using a supplied training set.

The training set is imbalanced between two classes.

You need to resolve the data imbalance.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Select all that apply, then click Submit answer.

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Penalize the classification
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Resample the dataset using undersampling or oversampling
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Normalize the training feature set
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Generate synthetic samples in the minority class
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Use accuracy as the evaluation metric of the model

Reference / correct answer:

Penalize the classification

Resample the dataset using undersampling or oversampling

Generate synthetic samples in the minority class

A: Try Penalized Models

You can use the same algorithms but give them a different perspective on the problem.

Penalized classification imposes an additional cost on the model for making classification mistakes on the minority class during training. These penalties can bias the model to pay more attention to the minority class.

B: You can change the dataset that you use to build your predictive model to have more balanced data.

This change is called sampling your dataset and there are two main methods that you can use to even-up the classes: Consider testing under-sampling when you have an a lot data (tens- or hundreds of thousands of instances or more) Consider testing over-sampling when you don't have a lot of data (tens of thousands of records or less)

D: Try Generate Synthetic Samples

A simple way to generate synthetic samples is to randomly sample the attributes from instances in the minority class.

Reference:

https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

Question 11 (Mixed Questions)

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

Select all that apply, then click Submit answer.

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a Root Mean Square Error value that is low
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an R-Squared value close to 0
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an F1 score that is low
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an R-Squared value close to 1
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an F1 score that is high
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a Root Mean Square Error value that is high

Question 12 (New Update)

You have a Jupyter Notebook that contains Python code that is used to train a model.

You must create a Python script for the production deployment. The solution must minimize code maintenance.

Which two actions should you perform? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

Select all that apply, then click Submit answer.

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Refactor the Jupyter Notebook code into functions
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Save each function to a separate Python file
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Define a main() function in the Python script
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Remove all comments and functions from the Python script

DP-100 – Designing and Implementing a Data Science Solution on Azure