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Question No 1

A machine learning engineer has created a Feature Table new_table using Feature Store Client fs. When creating the table, they specified a metadata description with key information about the Feature Table. They now want to retrieve that metadata programmatically. Which of the following lines of code will return the metadata description?

Choose the Choices:

There is no way to return the metadata description programmatically.

fs.create_training_set("new_table")

fs.get_table("new_table").description

fs.get_table("new_table").load_df()

fs.get_table("new_table")

Question No 2

A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column price is greater than 0. Which of the following code blocks will accomplish this task?

Choose the Choices:

spark_df[spark_df["price"] > 0]

spark_df.filter(col("price") > 0)

SELECT * FROM spark_df WHERE price > 0

spark_df.loc[spark_df["price"] > 0,]

spark_df.loc[,spark_df["price"] > 0]

Question No 3

A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model. Which of the following classification metrics should be used to evaluate the model?

Choose the Choices:

Question No 4

In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?

Choose the Choices:

When the features are of the categorical type

When the features are of the boolean type

When the features contain a lot of extreme outliers

When the features contain no outliers

When the features contain no missing no values

Question No 5

A data scientist has replaced missing values in their feature set with each respective feature variable’s median value. A colleague suggests that the data scientist is throwing away valuable information by doing this. Which of the following approaches can they take to include as much information as possible in the feature set?

Choose the Choices:

Impute the missing values using each respective feature variable's mean value instead of the median value

Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them

Remove all feature variables that originally contained missing values from the feature set

Create a binary feature variable for each feature that contained missing values indicating whether each row's value has been imputed

Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing

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