AWS Certified AI Practitioner AIF-C01 Practice Question
A small development team needs to add speech-to-text capability to its mobile app within days. They have no annotated audio data or ML expertise. Which source of model will minimize development time and data requirements?
Collect and label a large audio dataset, then train a custom deep-learning model in Amazon SageMaker.
Design a new neural network architecture and allow it to learn from unlabeled user audio without supervision.
Generate synthetic audio clips and use them to train a reinforcement learning agent for speech recognition.
Deploy an open-source pre-trained speech recognition model that is already available in a public model hub.
Open-source pre-trained models have already been trained on large, diverse datasets and are published for reuse. Deploying one of these models (for example, a speech-to-text model from a public model hub or Amazon SageMaker JumpStart) requires no data collection or training, so the team can integrate the capability quickly. Training a custom model from scratch, creating synthetic data for reinforcement learning, or relying on an unsupervised model would all demand significant time, expertise, and/or labeled data that the team does not have.
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AWS Certified AI Practitioner AIF-C01
Fundamentals of AI and ML
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