An organization needs large data sets to perform application testing. Which of the following would BEST fulfill this need?

The correct answer is C. Using open-source data repositories. Open-source data repositories (e.g., UCI ML Repository, Kaggle, government data portals) provide large, publicly licensed datasets that can be freely used for testing purposes without privacy, IP, or compliance concerns. They are purpose-built for this kind of use. AI model…

AI Security Design and Implementation

Question

Options

AReviewing AI model cards
BIncorporating data from search content
CUsing open-source data repositories
DPerforming AI data augmentation

How the community answered

(26 responses)

A
8% (2)
C
88% (23)
D
4% (1)

Explanation

Open-source data repositories (e.g., UCI ML Repository, Kaggle, government data portals) provide large, publicly licensed datasets that can be freely used for testing purposes without privacy, IP, or compliance concerns. They are purpose-built for this kind of use. AI model cards (A) describe model metadata, not data sources. Search content (B) raises copyright and data quality issues. Data augmentation (D) generates variations of existing data but requires a base dataset to start with-it does not fulfill the need for a large initial dataset from scratch.

Topics

#Data sourcing#Open-source data#AI testing data#Application testing

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