Answer: Distributing malicious flash drives in a parking lot or other high-traffic area, often with a label that will tempt the person who finds it into plugging it in, is a technique used by penetration testers.
Explanation:
One of the most often used methods of linear dimension reduction is principal component analysis (PCA). It can be used both on its own and as a starting point for further dimension reduction techniques.
By projecting the data onto a set of orthogonal axes, the projection-based PCA approach changes the data. An unsupervised linear transformation method known as Principal Component Analysis (PCA) is frequently utilized in a variety of domains, most notably for feature extraction and dimensionality reduction. Data compression, made possible by dimensionality reduction, results in less storage space being used. It speeds up computation. It also aids in removing any extraneous features. Since PCA is a variance-maximizing activity, normalization is crucial. our original data is projected in a manner that maximizes variance.
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Answer:
use and feedback
Explanation:
Once this is completed the software product is delivered to the user for use and feedback. If both testing and bug corrections have been completed it means that the product is finalized and working as intended. Once this is the case, all that is left for a development team to do is deliver the final product to the end-user for them to use and give their feedback as to how they like the product. Many times this feedback can restart the testing and bug correction phase once again if the development team decides to add new features.