Databricks Machine Learning Associate Question 565
Select 4You are tasked with building a training pipeline on Databricks to train a machine learning model on a large dataset. The pipeline must include data preprocessing, model training, and hyperparameter tuning using MLflow. Which of the following steps should be included in your pipeline to achieve this?
- A
Use Databricks Auto Loader to ingest the dataset into a Delta table
- B
Perform data preprocessing using a combination of PySpark transformations and MLlib feature transformers
- C
Log hyperparameter configurations, metrics, and model artifacts to MLflow during training
- D
Train the model using the Databricks SQL interface
- E
Include a cross-validation step to evaluate multiple hyperparameter configurations
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Building a robust training pipeline on Databricks requires combining data ingestion, preprocessing, model training, and hyperparameter tuning. Databricks provides tools such as Auto Loader for efficient data ingestion, PySpark and MLlib for preprocessing, and MLflow for tracking experiments. Cross-validation is a necessary step for effective hyperparameter tuning, while the Databricks SQL interface is not relevant for model training in this context.
- A. Correct.
Using Databricks Auto Loader to ingest the dataset ensures efficient and scalable data loading, which is a key part of a training pipeline.
- B. Correct.
Preprocessing data is crucial for preparing it for model training, and PySpark transformations and MLlib feature transformers are commonly used tools in Databricks for this purpose.
- C. Correct.
Logging hyperparameter configurations, metrics, and model artifacts to MLflow ensures that the entire training process is tracked and reproducible, which is a best practice in machine learning workflows.
- D. Incorrect.
The Databricks SQL interface is not used for training machine learning models; it is primarily for querying and analyzing data.
- E. Correct.
Cross-validation is essential for robust hyperparameter tuning as it ensures that the model is evaluated across multiple splits of the dataset.