NEW QUESTION 37
A data scientist uses 3-fold cross-validation and the following hyperparameter grid when optimizing model hyperparameters via grid search for a classification problem:
* Hyperparameter 1: [2, 5, 10]
* Hyperparameter 2: [50, 100]
Which of the following represents the number of machine learning models that can be trained in parallel during this process?

NEW QUESTION 38
A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation when k > 2.
Which of the following describes a potential benefit of using a train-validation split over k-fold cross-validation in this scenario?

NEW QUESTION 39
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 discount is less than or equal 0.
Which of the following code blocks will accomplish this task?

NEW QUESTION 40
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?

NEW QUESTION 41
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?

NEW QUESTION 42
A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process.
Which change could the data scientist make to improve their model accuracy over the course of their tuning process?

NEW QUESTION 43
A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

NEW QUESTION 44
A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.
As a result, they have the following code block:

Which of the following changes do they need to make to the above code block in order to accomplish the task?

NEW QUESTION 45
A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process.
Which of the following feature engineering tasks will be the least efficient to distribute?

NEW QUESTION 46
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?

NEW QUESTION 47
Which of the following machine learning algorithms typically uses bagging?

NEW QUESTION 48
A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model by comparing the label predictions to the actual price values, the data scientist notices that the RMSE for the second model is much larger than the RMSE of the first model.
Which of the following possible explanations for this difference is invalid?

NEW QUESTION 49
A data scientist is using Spark ML to engineer features for an exploratory machine learning project.
They decide they want to standardize their features using the following code block:

Upon code review, a colleague expressed concern with the features being standardized prior to splitting the data into a training set and a test set.
Which of the following changes can the data scientist make to address the concern?

NEW QUESTION 50
A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run.
Which of the following approaches can the team use to identify which task is the cause of the failure?

NEW QUESTION 51
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?

NEW QUESTION 52
A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. The following root-mean-squared-error values are calculated on each of the validation folds:
* 10.0
* 12.0
* 17.0
Which of the following values represents the overall cross-validation root-mean-squared error?

NEW QUESTION 53
Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

NEW QUESTION 54
A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?

NEW QUESTION 55
A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:

They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:

Which of the following lines of code can be used to complete the code block to successfully complete the task?

NEW QUESTION 56
The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

NEW QUESTION 57
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?

NEW QUESTION 58
A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference.
In which situation will the machine learning engineer be correct?

NEW QUESTION 59
Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?

NEW QUESTION 60
Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?

NEW QUESTION 61
A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema:
prediction DOUBLE
actual DOUBLE
Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?