AWS Certified AI Practitioner AIF-C01 Practice Question
A startup is developing a multilingual voice assistant for global customers. To support responsible AI, the team wants to improve inclusivity during model training. Which action best addresses this goal?
Encrypt all user credentials both in transit and at rest during data collection.
Gather voice recordings from speakers with a wide range of accents and dialects before training the model.
Adjust the model's hyperparameters to minimize overfitting on the validation dataset.
Restrict the training data to phrases about the company's top-selling products to reduce dataset size.
Inclusivity focuses on ensuring an AI system works well for people from varied demographic, linguistic, and cultural backgrounds. Collecting voice samples that represent many accents and dialects expands the training data to cover multiple user groups, making the assistant more inclusive. Encrypting credentials protects privacy, tuning hyperparameters addresses overfitting, and limiting data to popular products narrows, rather than broadens, user representation-none of these actions directly advance inclusivity.
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What does inclusivity mean in the context of AI model training?
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Why is gathering diverse voice recordings critical for training a multilingual voice assistant?
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How does limiting training data to narrow topics impact inclusivity?
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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