AI 900 - pt 5

ensuring an AI system does not provide a prediction when important fields contain unusual or missing values is ​ (a)   principle for responsible AI

• reliability and safety
• inclusiveness
• privacy and security
• transparency

ensuring that the numeric variables in training data are on similar scale is an example of ​ (a)  

• feature selection
• data ingestion
• feature engineering
• model training

assigning classes to images before training a classification model is an example of ​ (a)  

• labeling
• evaluation
• feature engineering
• hyperparameter tuning

You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.

• yes

• no

You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.

• yes

• no

You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.

• yes

• no

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

• yes

• no

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

• yes

• no

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

• yes

• no

Which two actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? Each correct answer presents part of the solution.

• Calculate the accuracy of the model.

• Score test data by using the model.

• Combine multiple datasets

• Use the model for real-time predictions.

• Remove records that have missing values.