Free Databricks Databricks-Machine-Learning-Associate Practice Exam with Questions & Answers

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).

One-hot encoding transforms categorical variables into a format that can be provided to machine learning algorithms to better predict the output. However, when done prematurely or universally within a feature repository, it can be problematic:

• Dimensionality Increase:One-hot encoding significantly increases the feature space, especially with high cardinality features, which can lead to high memory consumption and slower computation.
• Model Specificity:Some models handle categorical variables natively (like decision trees and boosting algorithms), and premature one-hot encoding can lead to inefficiency and loss of information (e.g., ordinal relationships).
• Sparse Matrix Issue:It often results in a sparse matrix where most values are zero, which can be inefficient in both storage and computation for some algorithms.
• Generalization vs. Specificity:Encoding should ideally be tailored to specific models and use cases rather than applied generally in a feature repository.

References

• "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Max Kuhn and Kjell Johnson (CRC Press, 2019).

Gradient boosting is fundamentally an iterative algorithm where each new tree is built based on the errors of the previous ones. This sequential dependency makes it difficult to parallelize the training of trees in gradient boosting, as each step relies on the results from the preceding step. Parallelization in this context would undermine the core methodology of the algorithm, which depends on sequentially improving the model'sperformance with each iteration.References:

• Machine Learning Algorithms (Challenges with Parallelizing Gradient Boosting).

Gradient boosting is an ensemble learning technique that builds models in a sequential manner. Each new model corrects the errors made by the previous ones. This sequential dependency means that each iteration requires the results of the previous iteration to make corrections. Here is a step-by-step explanation of why this makes parallelization challenging:

• Sequential Nature: Gradient boosting builds one tree at a time. Each tree is trained to correct the residual errors of the previous trees. This requires the model to complete one iteration before starting the next.
• Dependence on Previous Iterations: The gradient calculation at each step depends on the predictions made by the previous models. Therefore, the model must wait until the previous tree has been fully trained and evaluated before starting to train the next tree.
• Difficulty in Parallelization: Because of this dependency, it is challenging to parallelize the training process. Unlike algorithms that process data independently in each step (e.g., random forests), gradient boosting cannot easily distribute the work across multiple processors or cores for simultaneous execution.

This iterative and dependent nature of the gradient boosting process makes it difficult to parallelize effectively.

References

• Gradient Boosting Machine Learning Algorithm
• Understanding Gradient Boosting Machines

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).

To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:

mlflow.register_model(f"runs:/{run_id}/model","best_model")

This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.

References

• MLflow documentation on model registry: https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model

To identify which task is causing the failure in the job, the team should review the matrix view in the Job's runs. The matrix view provides a clear and detailed overview of each task's status, allowing the team to quickly identify which task failed. This approach ismore efficient than running each notebook interactively, as it provides immediate insights into the job's execution flow and any issues that occurred during the run.

References:

• Databricks documentation on Jobs: Jobs in Databricks

AutoML platforms, such as the one available in Databricks Machine Learning, streamline various stages of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and model evaluation. However, exploratory data analysis (EDA) is typically performed outside the AutoML process. EDA involves understanding the dataset, visualizing distributions, identifying anomalies, and gaining insights into data before feeding it into a machine learning pipeline. This step is crucial for ensuring that the data is clean and suitable for model training but is generally done manually by the data scientist.

References

• Databricks documentation on AutoML: https://docs.databricks.com/applications/machine-learning/automl.html

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

To apply the Pandas UDFpredictto each record of a Spark DataFrame, you use themapInPandasmethod. This method allows the Pandas UDF to operate on partitions of the DataFrame as pandas DataFrames, applying the specified function (predictin this case) to each partition. The correct code completion to execute this is simplymapInPandas(predict), which specifies the UDF to use without additional arguments orincorrect function calls.References:

• PySpark DataFrame documentation (Using mapInPandas with UDFs).

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