Databricks Databricks-Machine-Learning-Associate Exam With Confidence Using Practice Dumps

The method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories.References:

• Feature Engineering Techniques (One-Hot Encoding).

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