Exam Certified Machine Learning Professional topic 1 question 20 discussion

Actual exam question from Databricks's Certified Machine Learning Professional

Question #: 20
Topic #: 1

[All Certified Machine Learning Professional Questions]

A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. log_artifact
B. log_model
C. log_metric
D. log_param
E. There is no way to store values like this.

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Suggested Answer: C 🗳️

by BokNinja at Dec. 19, 2023, 2:54 a.m.

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hugodscarvalho

10 months ago

Selected Answer: C

RMSE is a metric so we should use the inbuilt mlflow.log_metric(). Doc: https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_metric

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random_data_guy

11 months ago

Selected Answer: C

https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_metric import mlflow # Log a metric with mlflow.start_run(): mlflow.log_metric("mse", 2500.00)

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BokNinja

11 months, 1 week ago

C. import numpy as np from sklearn.metrics import mean_squared_error import mlflow # Assuming 'actual' is your array of actual values and 'pred' is your array of predicted values actual = ... pred = ... # Calculate RMSE rmse = np.sqrt(mean_squared_error(actual, pred)) # Log RMSE metric in MLflow mlflow.log_metric("rmse", rmse)

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Exam Certified Machine Learning Professional topic 1 question 20 discussion

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