Databricks Machine Learning Professional Question 85
Single answerYou are managing the lifecycle of a machine learning model in Databricks and need to transition it from development to production. You notice that the model is currently in the 'Staging' stage. What does this stage signify and what would be the most appropriate next step to prepare it for production deployment?
- A
The 'Staging' stage indicates the model is being tested and validated, and the next step is to transition it to 'Production' if it passes all tests.
- B
The 'Staging' stage indicates the model is finalized and ready for deployment, so no further action is needed.
- C
The 'Staging' stage indicates the model is still under experimentation and is not yet ready for validation or deployment.
- D
The 'Staging' stage indicates the model has been archived and is no longer active, so it cannot be used for deployment.
Show answer and explanation
Correct answer: A
Explanation
The Databricks model lifecycle uses stages to manage transitions between development, testing, and deployment. The 'Staging' stage is meant for testing and validation of the model. Once the model passes these processes, it can be promoted to 'Production'. This ensures a systematic and reliable approach to deploying models in production environments.
- A. Correct.
Correct. The 'Staging' stage in Databricks indicates that the model is undergoing testing and validation. Once it passes all tests, it can be promoted to the 'Production' stage for deployment.
- B. Incorrect.
Incorrect. The 'Staging' stage does not indicate that the model is finalized; it specifically represents the phase where the model is being validated before it can transition to 'Production'.
- C. Incorrect.
Incorrect. The 'Staging' stage is not for experimentation; this typically occurs before the model is registered or assigned a stage.
- D. Incorrect.
Incorrect. The 'Staging' stage does not mean the model is archived. Archived models are marked with a different status and are not active.