Databricks-Machine-Learning-Associate 무료 덤프문제 온라인 액세스

A data scientist is using the following code block to tune hyperparameters for a machine learning model:

Which change can they make the above code block to improve the likelihood of a more accurate model?

• A.Change sparkTrials() to Trials()
• B.Increase num_evals to 100
• C.Change tpe.suggest to random.suggest
• D.Change fmin() to fmax()

A data scientist has replaced missing values in their feature set with each respective feature variable's median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.
Which of the following approaches can they take to include as much information as possible in the feature set?

• A.Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them
• B.Impute the missing values using each respective feature variable's mean value instead of the median value
• C.Create a binary feature variable for each feature that contained missing values indicating whether each row's value has been imputed
• D.Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing
• E.Remove all feature variables that originally contained missing values from the feature set

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.Scikit-learn
• B.Spark ML
• C.PyTorch
• D.Keras

A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, 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. All parent and child runs are being manually started with mlflow.start_run.
Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

• A.They can turn on Databricks Autologging
• B.They can start each child run inside the parent run's indented code block using mlflow.start runO
• C.They can specify nested=True when starting the child run for each unique combination of hyperparameter values
• D.They can start each child run with the same experiment ID as the parent run
• E.They can specify nested=True when starting the parent run for the tuning process

Which of the following statements describes a Spark ML estimator?

• A.An estimator is a hyperparameter arid that can be used to train a model
• B.An estimator is an alqorithm which can be fit on a DataFrame to produce a Transformer
• C.An estimator is an evaluation tool to assess to the quality of a model
• D.An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions
• E.An estimator chains multiple alqorithms toqether to specify an ML workflow