Q: 7
You're working with petabytes of data and need to make this dataset more manageable. To do this,
you want to reduce the number of variables under consideration.
What is the name for this process?
Options
Discussion
A imo, had something like this in a mock. Dimensionality reduction is literally the process for cutting down variables, not rows. Pretty sure that's what PMI wants here, but open to pushback if I'm missing some nuance.
Had something like this in a mock before, and A (Dimensionality Reduction) was the pick since it specifically means dropping variables/features to simplify huge datasets. Gradient descent is more about optimization, not reducing variables. Pretty sure it's A, but let me know if you see it differently.
Why is D even listed? I keep seeing "data selection" as an option in exam reports. D
Probably A here. When it's about reducing variables, that's classic dimensionality reduction stuff, not data selection.
A saw this in a similar exam question and it matched the key term for reducing variables.
Option A, not D-dimensionality reduction is all about cutting variables. Data selection just picks data, easy to mix up.
D . Data selection gets used a lot as a concept trap in questions about making big datasets easier to work with.
Its A
These PMI questions always love throwing in D just to trip us up, but it's A imo. Dimensionality reduction is about cutting variables not rows. If you see "variables," think A here.
Probably A here. Dimensionality reduction is all about cutting down the number of variables or features. If they were asking about reducing data points or rows, then D could be in play, but the key word is "variables." I think A is right but let me know if someone sees a twist.
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