Databricks-Machine-Learning-Associate Databricks Certified Machine Learning Associate Exam Questions and Answers

To find the run_id of the run with the best root-mean-square error (RMSE) in an MLflow experiment, the correct line of code to use is:

mlflow.search_runs( experiment_id, order_by=["metrics.rmse"] )["run_id"][0]

This line of code searches the runs in the specified experiment, orders them by the RMSE metric in ascending order (the lower the RMSE, the better), and retrieves the run_id of the best-performing run. Option C correctly represents this logic.

References

• MLflow documentation on tracking experiments: https://www.mlflow.org/docs/latest/python_api/mlflow.html#mlflow.search_runs

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

By creating a binary feature variable for each feature with missing values to indicate whether a value has been imputed, the data scientist can preserve information about the original state of the data. This approach maintains the integrity of the dataset by marking which values are original and which are synthetic (imputed). Here are the steps to implement this approach:

• Identify Missing Values:Determine which features contain missing values.
• Impute Missing Values:Continue with median imputation or choose another method (mean, mode, regression, etc.) to fill missing values.
• Create Indicator Variables:For each feature that had missing values, add a new binary feature. This feature should be '1' if the original value was missing and imputed, and '0' otherwise.
• Data Integration:Integrate these new binary features into the existing dataset. This maintains a record of where data imputation occurred, allowing models to potentially weight these observations differently.
• Model Adjustment:Adjust machine learning models to account for these new features, which might involve considering interactions between these binary indicators and other features.

References

• "Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari (O'Reilly Media, 2018), especially the sections on handling missing data.
• Scikit-learn documentation on imputing missing values: https://scikit-learn.org/stable/modules/impute.html

Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios.References:

• Spark MLlib documentation (Feature Engineering with Spark ML).

In Spark ML, the linear regression model expects the feature column to be a vector type. However, if thefeaturescolumn in the DataFrametrain_dfis not already in this format (such as being a column of type UDT or a non-vectorized type), the engineerneeds to convert it to a vector column using a transformer likeVectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.

References

• Spark MLlib documentation forLinearRegression:https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression

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 determine the number of machine learning models that can be trained in parallel, we need to calculate the total number of combinations of hyperparameters. The given hyperparameter grid includes:

• Hyperparameter 1: [2, 5, 10] (3 values)
• Hyperparameter 2: [50, 100] (2 values)

The total number of combinations is the product of the number of values for each hyperparameter:3 (values of Hyperparameter 1)×2 (values of Hyperparameter 2)=63 (values of Hyperparameter 1)×2 (values of Hyperparameter 2)=6

With 3-fold cross-validation, each combination of hyperparameters will be evaluated 3 times. Thus, the total number of models trained will be:6 (combinations)×3 (folds)=186 (combinations)×3 (folds)=18

However, the number of models that can be trained in parallel is equal to the number of hyperparameter combinations, not the total number of models considering cross-validation. Therefore, 6 models can be trained in parallel.

References:

• Databricks documentation on hyperparameter tuning: Hyperparameter Tuning

For large datasets with many variables, Spark ML distributes the training of a linear regression model using iterative optimization methods. Specifically, Spark ML employs algorithms such as Gradient Descent or L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) to iteratively minimize the loss function. These iterative methods are suitable for distributed computing environments and can handle large-scale data efficiently by partitioning the data across nodes in a cluster and performing parallel updates.References:

• Spark MLlib Documentation (Linear Regression with Iterative Optimization).

Including the entire pipeline as the estimator in the cross-validation process means that all stages of the pipeline, including data preprocessing steps like string indexing and vector assembling, will be refit or retransformed for each fold of the cross-validation. This results in a longer runtime because each fold requires re-execution of these preprocessing steps, which can be computationally expensive.

If only the random forest regressor (rfr) were included as the estimator, the preprocessing steps would be performed once, and only the model fitting would be repeated for each fold, significantly reducing the computational overhead.

References:

• Databricks documentation on cross-validation: Cross Validation

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance.References:

• Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).

TheSparkTrials()is used to distribute trials of hyperparameter tuning across a Spark cluster. If the environment does not support Spark or if the user prefers not to usedistributed computing for this purpose, switching toTrials()would be appropriate.Trials()is the standard class for managing search trials in Hyperopt but does not distribute the computation. If the user is encountering issues withSparkTrials()possibly due to an unsupported configuration or an error in the cluster setup, usingTrials()can be a suitable change for running the optimization locally or in a non-distributed manner.

References

• Hyperopt documentation: https://hyperopt.github.io/hyperopt/

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.

References:

• Databricks documentation on Random Forest: Random Forest in Spark ML

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).

The Root Mean Squared Error (RMSE) is a standard and widely used metric for evaluating the accuracy of regression models. The statement that it is invalid is incorrect. Here’s a breakdown of why the other statements are or are not valid:

• Transformations and RMSE Calculation:If the model predictions were transformed (e.g., using log), they should be converted back to their original scale before calculating RMSE to ensure accuracy in the evaluation. Missteps in this conversion process can lead to misleading RMSE values.
• Accuracy of Models:Without additional information, we can't definitively say which model is more accurate without considering their RMSE values properly scaled back to the original price scale.
• Appropriateness of RMSE:RMSE is entirely valid for regression problems as it provides a measure of how accurately a model predicts the outcome, expressed in the same units as the dependent variable.

References

• "Applied Predictive Modeling" by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the chapters discussing model evaluation metrics.

Thesummary()function in PySpark's DataFrame API provides descriptive statistics which include count, mean, standard deviation, min, max, and quantiles for numeric columns. Here are the steps on how it can be used:

• Import PySpark:Ensure PySpark is installed and correctly configured in the Databricks environment.
• Load Data:Load the data into a Spark DataFrame.
• Apply Summary:Usespark_df.summary()to generate summary statistics.
• View Results:The output from thesummary()function includes the statistics specified in the query (count, mean, standard deviation, min, max, and potentially quartiles which approximate the interquartile range).

References

• PySpark Documentation:https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.summary.html

Imputing missing values with the median is often preferred over the mean in scenarios where the data contains a lot of extreme outliers. The median is a more robust measure of central tendency in such cases, as it is not as heavily influenced by outliers as the mean. Using the median ensures that the imputed values are more representative of the typical data point, thus preserving the integrity of the dataset's distribution. The other options are not specifically relevant to the question of handling outliers in numerical data.References:

• Data Imputation Techniques (Dealing with Outliers).

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

For the feature set schema provided, the columns that need to be imputed using the most common value (mode) are typically the categorical columns. In this case,loyalty_tieris the only categorical column that should be imputed using the most common value.customer_idis a unique identifier and should not be imputed, whilespendandunitsare numerical columns that should typically be imputed using the mean or median values, not the mode.

References:

• Databricks documentation on missing value imputation: Handling Missing Data

If you need any further clarification or additional questions answered, please let me know!

When evaluating the RMSE for a model that predicts log-transformed prices, the predictions need to be transformed back to the original scale to obtain an RMSE that is comparable with the actual price values. This is done by exponentiating the predictions before computing the RMSE. The RMSE should be computed on the same scale as the original data to provide a meaningful measure of error.

References:

• Databricks documentation on regression evaluation: Regression Evaluation