Ultimate access to all questions.
Upgrade Now 🚀
Sign in to unlock AI tutor
After successfully training and evaluating a machine learning model with Databricks MLlib, a data scientist is ready to deploy the model for real-time predictions in a production environment. What is the recommended approach?
A
Save the model to a Delta table and query it for predictions.
B
Schedule a Databricks Job to run the model periodically.
C
Export the model as a serialized file and deploy it on a separate server.
D
Use MLflow to package the model and deploy it as a REST API endpoint.
