AI-900_Modulo_2

A bank wants to use historic loan repayment records to categorize loan applications as low-risk or high-risk based on characteristics like the loan amount, the income of the borrower, and the loan period. What kind of machine learning model should the bank use automated machine learning to create?

• Classification

• Regression

• Time series forecasting

You want to use automated machine learning to train a regression model with the best possible R2 score. How should you configure the automated machine learning experiment?

• Set the Primary metric to R2 score

• Block all algorithms other than GradientBoosting

• Enable featurization

You are creating a training pipeline for a regression model, using a dataset that has multiple numeric columns in which the values are on different scales. You want to transform the numeric columns so that the values are all on a similar scale based relative to the minimum and maximum values in each column. Which module should you add to the pipeline?

• Select Columns in a Dataset

• Normalize Data

• Clean Missing Data

Why do we split our data into training and validation sets?

• Data is split into two sets in order to create two models, one model using the training set and a different model using the validation set.

• Splitting data into two sets enables you to compare the labels that the model predicts with the actual known labels in the original dataset.

• We only need to split our data when we use the Azure Machine Learning Designer, not in other machine learning scenarios.

You are using Azure Machine Learning designer to create a training pipeline for a binary classification model. You have added a dataset containing features and labels, a Two-Class Decision Forest module, and a Train Model module. You plan to use Score Model and Evaluate Model modules to test the trained model with a subset of the dataset that was not used for training. Which additional kind of module should you add?

• Join Data

• Split Data

• Select Columns in Dataset

You use an Azure Machine Learning designer pipeline to train and test a binary classification model. You review the model's performance metrics in an Evaluate Model module, and note that it has an AUC score of 0.3. What can you conclude about the model?

• The model can explain 30% of the variance between true and predicted labels.

• The model predicts accurately for 70% of test cases.

• The model performs worse than random guessing.

You use Azure Machine Learning designer to create a training pipeline for a classification model. What must you do before deploying the model as a service?

• Create an inference pipeline from the training pipeline

• Add an Evaluate Model module to the training pipeline

• Clone the training pipeline with a different name

You are using an Azure Machine Learning designer pipeline to train and test a K-Means clustering model. You want your model to assign items to one of three clusters. Which configuration property of the K-Means Clustering module should you set to accomplish this?

• Set Number of Centroids to 3

• Set Random number seed to 3

• Set Iterations to 3

You use Azure Machine Learning designer to create a training pipeline for a clustering model. Now you want to use the model in an inference pipeline. Which module should you use to infer cluster predictions from the model?

• Score Model

• Assign Data to Clusters

• Train Clustering Model

Which of the following statements true machine learning?

• Machine learning is a technique that we can to create predictive models that continue to be accurate regardless of new and rapid data changes

• Machine learning is a technique we ca use to create predictive model model based on data relationships

• Machine learning is a technique we can use to create predictive models that do not rely on data relationships

• Machine learning is a technique we can use to create non-predictive models based on data relationships