Databricks Databricks-Machine-Learning-Associate Databricks Certified Machine Learning Associate Exam Exam Practice Test

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

If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.

In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.

References:

• Model Ensemble Techniques

The suggestion not to one-hot encode categorical feature variables within the feature repository is justified because one-hot encoding can be problematic for some machine learning algorithms. Specifically, one-hot encoding increases the dimensionality of the data, which can be computationally expensive and may lead to issues such as multicollinearity and overfitting. Additionally, some algorithms, such as tree-based methods, can handle categorical variables directly without requiring one-hot encoding.

References:

• Databricks documentation on feature engineering: Feature Engineering

The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential for machine learning and deep learning, including MLflow. To use it, the machine learning engineer can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime Version" dropdown menu while creating their cluster. This selection ensures that all necessary machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need to manually install them each time.

References

• Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html

Vectorized pandas UDFs, also known as Pandas UDFs, are a powerful feature in PySpark that allows for more efficient operations than standard UDFs. They operate by processing data in batches, utilizing vectorized operations that leverage pandas to perform operations on whole batches of data at once. This approach is much moreefficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation.

References

• PySpark Documentation on UDFs:https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_pandas.html#pandas-udfs-a-k-a-vectorized-udfs

Spark ML (part of Apache Spark's MLlib) is designed to handle machine learning tasks across multiple nodes in a cluster, effectively parallelizing tasks like hyperparameter tuning. It supports various machine learning algorithms that can be optimized over a Spark cluster, making it suitable for parallelizing hyperparameter tuning for single-node machine learning models when they are adapted to run on Spark.

References

• Apache Spark MLlib Guide:https://spark.apache.org/docs/latest/ml-guide.html

Spark ML is a library within Apache Spark designed for scalable machine learning. It provides tools to handle large-scale machine learning tasks, including parallelizing the hyperparameter tuning process for single-node machine learning models using a Spark cluster. Here’s a detailed explanation of how Spark ML can be used:

• Hyperparameter Tuning with CrossValidator: Spark ML includes theCrossValidatorandTrainValidationSplitclasses, which are used for hyperparameter tuning. These classes can evaluate multiple sets of hyperparameters in parallel using a Spark cluster.

from pyspark.ml.tuning import CrossValidator, ParamGridBuilder

from pyspark.ml.evaluation import BinaryClassificationEvaluator

# Define the model

model = ...

# Create a parameter grid

paramGrid = ParamGridBuilder() \

addGrid(model.hyperparam1, [value1, value2]) \

addGrid(model.hyperparam2, [value3, value4]) \

build()

# Define the evaluator

evaluator = BinaryClassificationEvaluator()

# Define the CrossValidator

crossval = CrossValidator(estimator=model,

estimatorParamMaps=paramGrid,

evaluator=evaluator,

numFolds=3)

• Parallel Execution: Spark distributes the tasks of training models with different hyperparameters across the cluster’s nodes. Each node processes a subset of the parameter grid, which allows multiple models to be trained simultaneously.
• Scalability: Spark ML leverages the distributed computing capabilities of Spark. This allows for efficient processing of large datasets and training of models across many nodes, which speeds up the hyperparameter tuning process significantly compared to single-node computations.

References

• Apache Spark MLlib Documentation
• Hyperparameter Tuning in Spark ML

The best approach for the data scientist to take in this scenario is to create a new branch in Databricks, commit their changes, and push those changes to the Git provider. This approach allows the data scientist to make updates and improvements to the feature engineering part of the preprocessing pipeline without affecting the main codebase that runs daily. By creating a new branch, they can work on their changes in isolation. Once the changes are ready and tested, they can be merged back into the main branch through a pull request, ensuring a smooth integration process and allowing for code review and collaboration with other team members.

References:

• Databricks documentation on Git integration: Databricks Repos

To view the notebook that was run to create an MLflow run, you can click the "Source" link in the row corresponding to the run in the MLflow experiment page. The "Source" link provides a direct reference to the source notebook or script that initiated the run, allowing you to review the code and methodology used in the experiment. This feature is particularly useful for reproducibility and for understanding the context of the experiment.References:

• MLflow Documentation (Viewing Run Sources and Notebooks).

If the model object is passed to the estimator parameter ofCrossValidatorand the cross-validation object itself is placed as a stage in the pipeline, the feature engineering stages within the pipeline would be applied separately to each training and validation fold during cross-validation. This leads to a significant issue: the feature engineering stages would be computed using validation data, thereby leaking information from the validation set into the training process. This would potentially invalidate the cross-validation results by giving an overly optimistic performance estimate.References:

• Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).

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: http://hyperopt.github.io/hyperopt/

The machine learning engineer can transition a model version to the Production stage in the Model Registry from the model version page. This page provides detailed information about a specific version of a model, including its metrics, parameters, and current stage. From here, the engineer can perform stage transitions, moving the model from Staging to Production after it has passed all necessary tests.

References

• Databricks documentation on MLflow Model Registry: https://docs.databricks.com/applications/mlflow/model-registry.html#model-version

To organize MLflow runs with one parent run for the tuning process and a child run for each unique combination of hyperparameter values, the data scientist can specifynested=Truewhen starting the child run. This approach ensures that each child run is properly nested under the parent run, maintaining a clear hierarchical structure for the experiment. This nesting helps in tracking and comparing different hyperparameter combinations within the same tuning process.References:

• MLflow Documentation (Managing Nested Runs).

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 method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories.References:

• Feature Engineering Techniques (One-Hot Encoding).

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

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).

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

The code block provided by the machine learning engineer will perform the desired inference when the Feature Store feature set was logged with the model at model_uri. This ensures that all necessary feature transformations and metadata are available for the model to make predictions. The Feature Store in Databricks allows for seamless integration of features and models, ensuring that the required features are correctly used during inference.

References:

• Databricks documentation on Feature Store: Feature Store in Databricks

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

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

For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.

References:

• Databricks documentation on clusters: Clusters in Databricks

TheSparkTrialsclass in the Hyperopt library allows for parallel hyperparameter optimization on a Spark cluster. This enables efficient tuning of hyperparameters by distributing the optimization process across multiple nodes in a cluster.

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