AWS Certified Cloud Practitioner

Explanation

Correct Answers: A and C

A. Detect imbalances or disparities in the data. - This is crucial for ensuring fairness and reducing bias in machine learning models. By identifying imbalances in the data, companies can address potential biases that could lead to unfair outcomes.

C. Evaluate the model's behavior so that the company can provide transparency to stakeholders. - This is essential for responsible AI practices. Evaluating model behavior helps understand how decisions are made, which is necessary for providing transparency and accountability to stakeholders.

Why other options are incorrect:

B. Ensure that the model runs frequently. - While operational efficiency is important, it doesn't directly address fairness, bias reduction, or transparency requirements.

D. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate. - ROUGE is specifically for evaluating text summarization quality, not for ensuring fairness or transparency. Also, no model can be guaranteed to be 100% accurate.

E. Ensure that the model's inference time is within the accepted limits. - This relates to performance optimization but doesn't directly address fairness, bias, or transparency requirements.

These selections align with responsible AI practices focusing on fairness, bias mitigation, and transparency rather than just operational or performance metrics.