Cloud Kicks wants to use an AI model to predict the demand for shoes using historical data on sales and regional characteristics. What is an essential data quality dimension to achieve this goal?
I asked this question to chatGPT and here is the reply
The correct answer is B. Reliability.
For Cloud Kicks to use an AI model to accurately predict shoe demand, reliable data is essential. Reliability refers to the consistency and accuracy of the data, ensuring that the historical sales data and regional characteristics are trustworthy and can lead to accurate predictions.
Here’s why the other options are less relevant:
A. Age: While the age or recency of data can be important, it is not the most essential data quality dimension in this case. Older data can still be useful if it's reliable and relevant.
C. Volume: Having a large amount of data (volume) is helpful for AI models, but without reliability, even large datasets can lead to poor predictions.
Thus, reliable data is crucial to ensure the AI model makes accurate and consistent predictions.
I believe letter B because "Reliability" is kinda synonym for "Accuracy" and "Consistency", which are both more related to the theme of the question than "Age".
A is the correct and only choice that is a "Data quality dimension".
I get how B seems like it would be correct but this question isn't asking you to make up your own "data quality dimensions" its asking out of the 6 (Age, Completeness, Accuracy, Consistency, Duplication, Usage) which is appropriate here.
Please stop confidently writing the wrong answer (B) with no explanation and no evidence.
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