Databricks Databricks-Machine-Learning-Associate Exam With Confidence Using Practice Dumps

For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.

References:

• Databricks documentation on clusters: Clusters in Databricks

To calculate the overall cross-validation root-mean-squared error (RMSE), you average the RMSE values obtained from each validation fold. Given the RMSE values of 10.0, 12.0, and 17.0 for the three folds, the overall cross-validation RMSE is calculated as the average of these three values:

Overall CV RMSE=10.0+12.0+17.03=39.03=13.0Overall CV RMSE=310.0+12.0+17.0​=339.0​=13.0

Thus, the correct answer is 13.0, which accurately represents the average RMSE across all folds.References:

• Cross-validation in Regression (Understanding Cross-Validation Metrics).

To address the concern about standardizing features prior to splitting the data, the correct approach is to use the Pipeline API to ensure that only the training data's summary statistics are used to standardize the test data. This is achieved by fitting the StandardScaler (or any scaler) on the training data and then transforming both the training and test data using the fitted scaler. This approach prevents information leakage from the test data into the model training process and ensures that the model is evaluated fairly.References:

• Best Practices in Preprocessing in Spark ML (Handling Data Splits and Feature Standardization).