AWS Certified AI Practitioner Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose B3

  A) Increase the number of epochs (not correct) Increasing epochs just trains a model longer and does not adapt a pre-trained model to a new task B) Use transfer learning (is correct) Transfer learning takes an existing pre-trained model and fine-tunes it for a new but related task, avoiding training from scratch C) Decrease the number of epochs (not correct) Reducing epochs limits training and does not help reuse or adapt pre-trained models D) Use unsupervised learning (not correct) Unsupervised learning finds patterns without labeled data and does not specifically adapt pre-trained models for new tasks

• Chose B1

  Transfer learning lets you adapt pre-trained models to new tasks without starting from scratch, saving time and resources.

• Chose B10

  Transfer learning allows you to leverage pre-trained models (which have already been trained on large datasets) and adapt them for new, related tasks with minimal additional training. This strategy is highly efficient because it saves time and computational resources compared to training a model from scratch. By fine-tuning the pre-trained model on a smaller dataset specific to the new task, the model can learn task-specific features while maintaining the general knowledge it acquired during its initial training.