Ooh, edge data services, that's the hot new thing, isn't it? I'm gonna go with D and B too. Filtering and normalizing that data right at the source is super important, and supporting all the different APIs and interfaces is key for making it all work together. Though I gotta say, the other options sound kind of boring in comparison. Where's the machine learning and advanced analytics, am I right?
Alright, let's think this through. Edge data services, that's all about processing data closer to the source, right? So D is definitely one of the key functionalities - gotta get that data cleaned up and ready to use before it even leaves the edge. And B, well, that's just good design, isn't it? Gotta play nice with all the other tech out there. I'm feeling pretty confident about those two.
Hmm, this is a tricky one. I was studying for this exam and I'm pretty sure edge data services are all about bringing the data crunching closer to where it's actually being used, you know? So I'd say D is a no-brainer - filtering, normalizing, and aggregating that data right at the edge is key. And B sounds right too, gotta have those multiple interfaces to talk to all the different devices and systems.
Whoa, edge data services, that sounds like something out of a sci-fi movie! But hey, I bet it's pretty important stuff for the tech-savvy folks out there. Let's see, I'm thinking D and B are the way to go. Filtering and normalizing data seems crucial, and supporting lots of interfaces and APIs makes sense if you want to play nice with all the other tech toys out there.
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