Free Practice Questions for Databricks Databricks-Machine-Learning-Associate Exam

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

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

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/

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

Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark. It allows users to perform pandas operations on large datasets using Spark's distributed compute capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and scalability.

References

pandas API on Spark documentation:https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

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).

In the provided code block, theImputerobject is created but not fitted on the data to generate anImputerModel. Thetransformmethod is being called directly on theImputerobject, which does not yet contain the fitted median values needed for imputation. The correct approach is to fit the imputer on the dataset first.

Corrected code:

imputer = Imputer( strategy="median", inputCols=input_columns, outputCols=output_columns ) imputer_model = imputer.fit(features_df)# Fit the imputer to the dataimputed_features_df = imputer_model.transform(features_df)# Transform the data using the fitted imputer

References:

PySpark ML Documentation

To improve the likelihood of a more accurate model, the data scientist can increasenum_evalsto 100. Increasing the number of evaluations allows the hyperparameter tuning process to explore a larger search space and evaluate more combinations of hyperparameters, which increases the chance of finding a more optimal set of hyperparameters for the model.

References:

Databricks documentation on hyperparameter tuning: Hyperparameter Tuning

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