Databricks-Machine-Learning-Associate Exam Dumps - Databricks ML Data Scientist Questions and Answers

The pandas API on Spark provides a way to scale pandas operations to big data while minimizing the need for refactoring existing pandas code. It allows users to run pandas operations on Spark DataFrames, leveraging Spark’s distributed computing capabilities to handle large datasets more efficiently. This approach requires minimal changes to the existing code, making it a convenient option for scaling pandas-based feature engineering notebooks.

References:

• Databricks documentation on pandas API on Spark: pandas API on Spark

Among the options listed, calculating the true median for imputing missing feature values is the least efficient to distribute. This is because the true median requires knowledge of the entire data distribution, which can be computationally expensive in a distributed environment. Unlike mean or mode, finding the median requires sorting the data or maintaining a full distribution, which is more intensive and often requires shuffling the data across partitions.

References

• Challenges in parallel processing and distributed computing for data aggregation like median calculation:https://www.apache.org

To compute the root mean-squared-error (RMSE) of a linear regression model using Spark ML, theRegressionEvaluatorclass is used. TheRegressionEvaluatoris specifically designed for regression tasks and can calculate various metrics, including RMSE, based on the columns containing predictions and actual values.

The correct code block to compute RMSE from thepreds_dfDataFrame is:

regression_evaluator = RegressionEvaluator( predictionCol="prediction", labelCol="actual", metricName="rmse") rmse = regression_evaluator.evaluate(preds_df)

This code creates an instance ofRegressionEvaluator, specifying the prediction and label columns, as well as the metric to be computed ("rmse"). It then evaluates the predictions inpreds_dfand assigns the resulting RMSE value to thermsevariable.

Options A and B incorrectly useBinaryClassificationEvaluator, which is not suitable for regression tasks. Option D also incorrectly usesBinaryClassificationEvaluator.

References:

• PySpark ML Documentation

To speed up the model tuning process when dealing with a large number of model configurations, parallelizing the hyperparameter search using Hyperopt is an effective approach. Hyperopt provides tools likeSparkTrialswhich can run hyperparameter optimization in parallel across a Spark cluster.

Example:

fromhyperoptimportfmin, tpe, hp, SparkTrials search_space = {'x': hp.uniform('x',0,1),'y': hp.uniform('y',0,1) }defobjective(params):returnparams['x'] **2+ params['y'] **2spark_trials = SparkTrials(parallelism=4) best = fmin(fn=objective, space=search_space, algo=tpe.suggest, max_evals=100, trials=spark_trials)

References:

• Hyperopt Documentation

The lack of improvement in model accuracy across evaluations suggests that the optimization algorithm might not be effectively exploring the hyperparameter space. Iterative optimization algorithms like Tree-structured Parzen Estimators (TPE) or Bayesian Optimization can adapt based on previous evaluations, guiding the search towards more promising regions of the hyperparameter space.

Changing the optimization algorithm can lead to better utilization of the information gathered during each evaluation, potentially improving the overall accuracy.

References:

• Hyperparameter Optimization with Hyperopt

The user wants to specify a search space for hyperparameters and let the tuning process randomly select values.GridSearchCVsystematically tries every combination of the provided hyperparameter values, which can be computationally expensive and time-consuming.RandomizedSearchCV, on the other hand, samples hyperparameters from a distribution for a fixed number of iterations. This approach is usually faster and still can find very good parameters, especially when the search space is large or includes distributions.

References

• Scikit-Learn documentation on hyperparameter tuning: https://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization

The functionmapInPandasin the PySpark DataFrame API allows for applying a function to each partition of the DataFrame. When working with grouped data,groupbyfollowed byapplyInPandasis the correct approach to apply a function to each group as a separate Pandas DataFrame. However, if the function should apply across each partition of the grouped data rather than on each individual group,mapInPandaswould be utilized. Since the code snippet indicates the use ofgroupby, the intent seems to be to applytrain_modelon each group specifically, which aligns withapplyInPandas. Thus,applyInPandasis a better fit to ensure that each group generated bygroupbyis processed through thetrain_modelfunction, preserving the partitioning and grouping integrity.

References

• PySpark Documentation on applying functions to grouped data:https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.GroupedData.applyInPandas.html

To display visual histograms and summaries of the numeric features in a Spark DataFrame, the Databricks utility functiondbutils.data.summarizecan be used. This function provides a comprehensive summary, including visual histograms.

Correct code:

dbutils.data.summarize(spark_df)

Other options likespark_df.describe()andspark_df.summary()provide textual statistical summaries but do not include visual histograms.

References:

• Databricks Utilities Documentation

The correct method to randomly split a Spark DataFrame into training and test sets is by using therandomSplitmethod. This method allows you to specify the proportions for the split as a list of weights and returns multiple DataFrames according to those weights. This is directly intended for splitting DataFrames randomly and is the appropriate choice for preparing data for training and testing in machine learning workflows.References:

• Apache Spark DataFrame API documentation (DataFrame Operations: randomSplit).

One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.References:

• Spark MLlib Documentation (Decision Trees and Quantile Binning).