Q: 3
An organization needs large data sets to perform application testing. Which of the following would
BEST fulfill this need?
Options
Discussion
C . Open-source data repositories literally exist to provide large, ready-to-use datasets. D is more for creating variation or synthetic data, not meeting the base requirement for large sets. Seen similar logic referenced in a few practice questions.
C
Probably C, unless the question is about generating unique test data instead of sourcing existing sets?
Probably C for this one.
Nah, I think C makes more sense than D. Data augmentation (D) is useful if you already have a dataset and need to expand it, but for just getting large data sets fast, open-source repositories (C) are built for this. Pretty sure that's what the exam wants, but open to debate.
C/D? I was thinking D since data augmentation can create even more data from what you have, which helps with scale in testing. But not sure if that matches the 'best fulfill' angle since you're starting from an existing set. Maybe someone who worked with both methods can weigh in.
Not B, C. Saw a similar question in an exam report, open-source repo fits best for large data.
Pretty clear it's C here.
Makes sense, I think C is the one here.
C tbh, D looks tempting for expansion but the question just wants large datasets, not synthetic ones.
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