Databricks DATABRICKS-MACHINE-LEARNING-ASSOCIATE Exam Questions & Answers

Correct Answer: D

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

Correct Answer: D

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

Correct Answer: C

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