I feel pretty confident about this one. Streaming with Spark is all about processing data incrementally as it arrives, so option D is the right answer. The key is that it's not waiting for a full batch to be processed.
Okay, I've got this. Streaming with Spark is all about processing data in real-time, so option B is the correct answer. The inference happens as soon as the data comes in, not in batches.
Hmm, this is a tricky one. I'm trying to remember the details about Spark's streaming capabilities. I think option D might be the right answer, but I'll need to double-check my notes to be sure.
I think the key here is understanding the difference between batch and streaming processing. Option A sounds like it could be correct, but I'm not 100% sure.
I'm a bit confused by the wording of these options. Can streaming really process "all types of records in real-time"? That sounds a bit broad to me. I'm leaning towards option A, but I'll need to think this through a bit more.
This is a good practice question. I'll carefully analyze the XML, the query options, and the expected output to determine the correct answer. Applying the XQuery concepts I've learned should help me solve this.
Haha, I bet the exam writer was having a real 'spark' of inspiration when they came up with these answer choices! But in all seriousness, I'm going with option D as well.
I'm torn between options B and D. Streaming should be about real-time inference, but the question specifically mentions Spark, so I'm not sure if option B is the best fit.
I think option D is the correct answer, as it describes the inference of incrementally processed records as soon as a trigger is hit. Streaming with Spark is all about processing data in real-time, not in batches.
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