Your task is classify if a company logo is present on an image. You found out that 96% of a data does not include a logo. You are dealing with data imbalance problem. Which metric do you use to evaluate to model?
I remember that when dealing with imbalanced data, focusing on recall can be crucial, so maybe the option with higher recall weighting is the way to go.
I think we practiced a similar question where we had to choose between precision and recall. I feel like in this case, recall might be more important since we want to catch logos.
I remember we discussed the importance of using metrics that account for class imbalance, but I'm not sure if it's the F1 Score or one of the F Scores with different weightings.
I'm leaning towards the external data sources or external objects options. Those sound like they would let the staff access the other apps without having to leave Salesforce, which is the key requirement here.
Hmm, I'm a bit unsure about this one. The options all look similar, but I need to make sure I understand the proper way to reference an external DTD file. Let me think this through carefully.
This seems like a pretty straightforward question about load balancing NMSP data across CMX servers. I think the key is to look for the configuration option that specifically mentions load balancing.
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