Certified Machine Learning Associate

To retrieve the metadata description of a Feature Table that was created using the Feature Store Client, you can access the description attribute of the table object. Therefore, the line of code 'fs.get_table("new_table").description' will return the metadata description.

To filter rows in a Spark DataFrame where the value in a specific column meets a certain condition, the `filter` method is used. The correct way to reference a column within this method is by using the `col` function from `pyspark.sql.functions`. Therefore, the correct code block to create a new DataFrame containing only rows where the 'price' column is greater than 0 is `spark_df.filter(col('price') > 0)`. Other provided options either use incorrect syntax or methods not suitable for a Spark DataFrame.

Recall should be used to evaluate the model since it measures the proportion of actual positive cases that were correctly identified. This is particularly important for the health organization because they want to maximize the number of positive cases detected, thereby ensuring fewer positive cases are missed.

When features contain a lot of extreme outliers, imputing missing values with the median is preferable over the mean because the median is less affected by extreme values. The mean can be skewed by these outliers, which would not provide an accurate central tendency of the data.

Creating a binary feature variable for each feature that contained missing values allows the model to identify whether any missing value was imputed. This retains the information about the missing values, which can sometimes hold predictive power and improve model performance.