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

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A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model in parallel. They elect to use the Hyperopt library to facilitate this process.
Which of the following Hyperopt tools provides the ability to optimize hyperparameters in parallel?

• A.fmin
• B.SparkTrials
• C.quniform
• D.search_space
• E.objective_function

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A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

• A.They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.
• B.They can check the Databricks Runtime ML box when creating their clusters.
• C.They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
• D.They can set the runtime-version variable in their Spark session to "ml".

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

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A data scientist has developed a machine learning pipeline with a static input data set using Spark ML, but the pipeline is taking too long to process. They increase the number of workers in the cluster to get the pipeline to run more efficiently. They notice that the number of rows in the training set after reconfiguring the cluster is different from the number of rows in the training set prior to reconfiguring the cluster.
Which of the following approaches will guarantee a reproducible training and test set for each model?

• A.Manually configure the cluster
• B.Write out the split data sets to persistent storage
• C.Set a speed in the data splitting operation
• D.Manually partition the input data

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