Databricks-Machine-Learning-Associate Practice Exam Questions with Answers Databricks Certified Machine Learning Associate Exam Certification

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

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

TheOneHotEncoderin Spark ML requires numerical indices as inputs rather than string labels. Therefore, you need to first convert the string columns to numerical indices usingStringIndexer. After that, you can applyOneHotEncoderto these indices.

Corrected code:

frompyspark.ml.featureimportStringIndexer, OneHotEncoder# Convert string column to indexindexers = [StringIndexer(inputCol=col, outputCol=col+"_index")forcolininput_columns] indexer_model = Pipeline(stages=indexers).fit(features_df) indexed_features_df = indexer_model.transform(features_df)# One-hot encode the indexed columnsohe = OneHotEncoder(inputCols=[col+"_index"forcolininput_columns], outputCols=output_columns) ohe_model = ohe.fit(indexed_features_df) ohe_features_df = ohe_model.transform(indexed_features_df)

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

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/

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

Increasing the level of parallelism from 4 to 8 cores can speed up the tuning process if each core can handle the entire dataset. This ensures that each core can independently work on training a model without running into memory constraints. If the entire dataset fits into the memory of each core, adding more cores will allow more models to be trained in parallel, thus speeding up the process.

References:

• Parallel Computing Concepts

For large datasets, Spark ML uses iterative optimization methods to distribute the training of a linear regression model. Specifically, Spark MLlib employs techniques like Stochastic Gradient Descent (SGD) and Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization to iteratively update the model parameters. These methods are well-suited for distributed computing environments because they can handle large-scale data efficiently by processing mini-batches of data and updating the model incrementally.

References:

• Databricks documentation on linear regression: Linear Regression in Spark ML

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

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

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

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).

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).

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

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

Using an iterator in thepandas_udfensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.

References:

• Databricks documentation on pandas UDFs: Pandas UDFs

To parallelize the inference of group-specific models using the Pandas Function API in PySpark, you can use theapplyInPandasfunction. This function allows you to apply a Python function on each group of a DataFrame and return a DataFrame, leveraging the power of pandas UDFs (user-defined functions) for better performance.

prediction_df = ( df.groupby("device_id") .applyInPandas(apply_model, schema=apply_return_schema) )

In this code:

• groupby("device_id"): Groups the DataFrame by the "device_id" column.
• applyInPandas(apply_model, schema=apply_return_schema): Applies theapply_modelfunction to each group and specifies the schema of the return DataFrame.

References:

• PySpark Pandas UDFs Documentation