Databricks-Machine-Learning-Associate VCE Exam Download

One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.References:

• Spark MLlib Documentation (Decision Trees and Quantile Binning).

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

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