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Practice exam covering Course 2: Create no-code predictive models with Azure Machine Learning Quiz Answers

Question 1)
In a machine learning algorithm, what method should you use to split data for training and evaluation?

• Use labels for training and features for evaluation
• Randomly split the data into rows for training and columns for evaluation
• Use features for training and labels for evaluation
• Randomly split the data into rows for training and rows for evaluation

Question 2)
Predicting how many hours of overtime a delivery person will work based on the number of orders received is an example of which machine learning model?

• Regression
• Classification
• Clustering

Question 3)
Predicting how many minutes it will take someone to run a race based on past race times is a use case for?

• Classification
• Clustering
• Regression

Question 4)
Let’s suppose you are working on an AI application that should predict the weather. From the dataset you have, you want to pick temperature and pressure to train the model. Which machine learning task enables you to do that?

• Feature engineering
• Model training
• Feature selection

Question 5)
True or False?
Azure Machine Learning designer supports custom JavaScript functions.

• True
• False

Question 6)
You can use AI systems to predict whether a student will complete a university course. Which machine learning type enables you to do that?

• Classification
• Clustering
• Regression

Question 7)
True or False?
Accuracy is always the primary metric used to measure a model’s performance. Is this true?

• True
• False

Question 8)
True or False?
Automated machine learning is the process of automating the time consuming, iterative tasks of machine learning model development.

• True
• False

Question 9)
Which module in the Azure Machine Learning designer should you use if you want to create a training dataset and a validation dataset from an existing dataset?

• Select columns in dataset
• Split data
• Join data
• Add rows

Question 10)
You want to create a CRM application that uses AI to segment customers into different groups to support a marketing department. Which machine learning type should you use?

• Clustering
• Regression
• Classification

Question 1)
What data values are influencing prediction models?

• Features
• Identifiers
• Dependent variables
• Labels

Question 3)
Imagine you work for a government institution that wants to predict the sea level in meters for the following 10 years. Which type of machine learning should you use?

• Regression
• Classification
• Clustering

Question 5)
True or False?
When working in Azure Machine Learning designer, it is possible to save your progress as a pipeline draft.

• True
• False

Question 6)
Predicting whether someone uses a bicycle to travel to work based on the distance from home to work is a use case for?

• Classification
• Clustering
• Regression

Question 7)
Which of the following metrics is used to evaluate a classification model?

• True positive rate
• Mean absolute error (MAE)
• Root mean squared error (RMSE)
• Coefficient of determination (R2)

Question 2)
Let’s suppose you want to create an AI system that can predict how many minutes late a flight will arrive based on the amount of snowfall at an airport. Which machine learning type should you use?

• Regression
• Classification
• Clustering

Question 4)
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models. Which two components can be dragged-and-dropped onto the canvas? Select all options that apply.

• Compute
• Dataset
• Pipeline
• Module

Question 8)
True or False?
Automated machine learning can automatically infer the training data from the use case provided.

• True
• False

Question 9)
True or False?
Azure Machine Learning designer provides a drag-and-drop visual canvas to build, test, and deploy machine learning models.

• True
• False

Question 10)
Fill in the blank.
__________ is a form of machine learning that has the capability to group similar items based on their features.

• Classification
• Regression
• Clustering