Online Access Free Databricks-Machine-Learning-Associate Practice Test

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.

A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy.

Which of the following suggestions should the team include in their guidelines?

• A.The F1 score should be utilized over accuracy when identifying true positives and true negatives are equally important to the business problem.
• B.The F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases.
• C.The F1 score should be utilized over accuracy when there are greater than two classes in the target variable.
• D.The F1 score should be utilized over accuracy when there is significant imbalance between positive and negative classes and avoiding false negatives is a priority.

A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?

• A.Parallelize with Hyperopt
• B.Implement MLflow Experiment Tracking
• C.Scale up with Spark ML
• D.Enable autoscaling clusters

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?

• A.spark_df.summary ()
• B.spark_df.printSchema()
• C.spark_df.stats()
• D.spark_df.describe().head()
• E.spark_df.toPandas()

A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin.
They use the following code block to create the objective_function:

Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?

• A.Remove the mean operation that is wrapping the cross_val_score operation
• B.Add test set validation process
• C.Replace the r2 return value with -r2
• D.Add a random_state argument to the RandomForestRegressor operation
• E.Replace the fmin operation with the fmax operation