Azure Data Scientists Associate Practice Exam

When dealing with multiple models, what is a key consideration for batch processing?

1. Always use the latest version.

2. Explicitly specify the model for clarity.

3. Storing results in multiple outputs.

4. Regularly deleting older models.

The correct answer is: Explicitly specify the model for clarity.

In the context of working with multiple models in batch processing, explicitly specifying the model is crucial for ensuring clarity and consistency in results. When you have multiple models, each with different characteristics, versions, and performance metrics, it's important to define which model is being used for each batch process. This specification helps to avoid ambiguity and ensures that stakeholders understand which model's predictions or analyses are being relied upon. By clearly stating the model in use, you facilitate reproducibility in experiments and production environments. This is especially important in a collaborative setting where different teams may be working with various models. Specifying the model mitigates risks related to version control issues and enhances traceability, which is vital for both debugging and validating outcomes. The other choices, while relevant to model management, do not provide the same level of assurance regarding clarity. Using the latest model may sometimes be beneficial, but it assumes that newer versions are always superior without considering model performance on specific datasets. Storing results in multiple outputs may help with organization but can lead to complexity and confusion if not properly managed. Regularly deleting older models can be part of a good model governance strategy, yet it can lead to loss of useful historical data that might be necessary for comparison or analysis. Therefore, specifying which model