Databricks Machine Learning Associate Question 60
Select 3You are working on a classification problem in Databricks and decide to use AutoML to automate the model development process. After running AutoML, you notice that the top-performing model is a random forest model. What steps can you take to further analyze or improve the selected AutoML model in Databricks?
- A
Review the MLflow experiment generated by AutoML to analyze the model's performance metrics and artifacts.
- B
Directly deploy the model to production without any further testing since AutoML guarantees optimal performance.
- C
Use the code generated by AutoML to customize the model training process, such as modifying hyperparameters or preprocessing steps.
- D
Export the trained model as a MLflow model and deploy it using a Databricks serving endpoint.
- E
Manually modify the AutoML notebook to experiment with alternative algorithms not explored by AutoML.
Show answer and explanation
Correct answers: A, C, D
Explanation
Databricks AutoML generates a notebook with the training code, MLflow experiment logs, and the selected model. This allows users to analyze performance, customize the pipeline, and deploy models effectively. However, further testing is recommended before production deployment, and manual modifications to explore new algorithms are unnecessary for the AutoML workflow.
- A. Correct.
Correct: AutoML in Databricks logs all experiments to MLflow, allowing you to review metrics, confusion matrices, and other artifacts for further analysis.
- B. Incorrect.
Incorrect: Although AutoML selects a strong performing model, it does not guarantee optimal performance in all scenarios. Further testing and validation are necessary before deployment.
- C. Correct.
Correct: AutoML generates a notebook with code that you can customize. This allows you to modify hyperparameters, preprocessing steps, or even certain aspects of the training pipeline.
- D. Correct.
Correct: AutoML-trained models can be exported as MLflow models, which are then deployable using Databricks serving endpoints for real-time inference.
- E. Incorrect.
Incorrect: While the AutoML notebook can be modified, the platform is designed to explore a wide range of algorithms by default. Manually adding new algorithms is not a standard or recommended practice.