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QUESTION: 1

Which of the following describes concept drift?

Concept drift is when there is a change in the distribution of an input variable
Concept drift is when there is a change in the distribution of a target variable
Concept drift is when there is a change in the relationship between input variables and target variables
Concept drift is when there is a change in the distribution of the predicted target given by the model
None of these describe Concept drift

Answer(s): D

A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.
Which of the following tools can the machine learning engineer use to assess their theory?

Kolmogorov-Smirnov (KS) test
One-way Chi-squared Test
Two-way Chi-squared Test
Jenson-Shannon distance
None of these

Answer(s): B

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QUESTION: 3

A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.

They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.

Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

Indent the child run blocks within the parent run block
Add the nested=True argument to the parent run
Remove the nested=True argument from the child runs
Provide the same name to the run name parameter for all three run blocks
Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs

Answer(s): E

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QUESTION: 4

A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.
Which of the following code blocks will accomplish this task inside of an existing MLflow run block?