Online Access Free Databricks-Machine-Learning-Associate Exam Questions

A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).
Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?

• A.
• B.
• C.
• D.

Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

• A.PvTorch
• B.Spark ML
• C.Scikit-learn
• D.pandas
• E.Keras

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?

• A.One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
• B.One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
• C.One-hot encoding is not a common strategy for representing categorical feature variables numerically.
• D.One-hot encoding is dependent on the target variable's values which differ for each apaplication.

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?

• A.spark_df.loc[spark_df["price"] > 0,:]
• B.SELECT * FROM spark_df WHERE price > 0
• C.spark_df[spark_df["price"] > 0]
• D.spark_df.loc[:,spark_df["price"] > 0]
• E.spark_df.filter(col("price") > 0)

A data scientist has created a linear regression model that uses log(price) as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFrame preds_df.
They are using the following code block to evaluate the model:
regression_evaluator.setMetricName("rmse").evaluate(preds_df)
Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable with price?

• A.They should exponentiate the predictions before computing the RMSE
• B.They should evaluate the MSE of the log predictions to compute the RMSE
• C.They should exponentiate the computed RMSE value
• D.They should take the log of the predictions before computing the RMSE