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Certified Machine Learning Associate
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Question 01
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?
Question 02
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?
Question 03
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?
Question 04
In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?
Question 05
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?
Question 06
A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature. Which of the following lines of code can the data scientist run to accomplish the task?
Question 07
An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository. Which of the following explanations justifies this suggestion?
Question 08
A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model by comparing the label predictions to the actual price values, the data scientist notices that the RMSE for the second model is much larger than the RMSE of the first model. Which of the following possible explanations for this difference is invalid?
Question 09
A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. The following root-mean-squared-error values are calculated on each of the validation folds: • 10.0 • 12.0 • 17.0 Which of the following values represents the overall cross-validation root-mean-squared error?
Question 10
A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block: Question exhibit A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model. Which of the following is a negative consequence of the approach suggested by the colleague?
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