Amazon Web Services AIF-C01 Dumps Questions Answers

Reinforcement learning allows a model to learn and improve over time based on feedback from its environment. In this case, the chatbot can improve its responses by being rewarded for positive customer feedback, which aligns well with the goal of self-improvement based on past interactions and new information.

Option B (Correct): "Reinforcement learning with rewards for positive customer feedback":This is the correct answer as reinforcement learning enables the chatbot to learn from feedback and adapt its behavior accordingly, providing self-improvement capabilities.

Option A:"Supervised learning with a manually curated dataset" is incorrect because it does not support continuous learning from new interactions.

Option C:"Unsupervised learning to find clusters of similar customer inquiries" is incorrect because unsupervised learning does not provide a mechanism for improving responses based on feedback.

Option D:"Supervised learning with a continuously updated FAQ database" is incorrect because it still relies on manually curated data rather than self-improvement from feedback.

AWS AI Practitioner References:

Reinforcement Learning on AWS:AWS provides reinforcement learning frameworks that can be used to train models to improve their performance based on feedback.

Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.

Data Augmentation for Imbalanced Classes:

Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.

Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.

Why Option A is Correct:

Balances Data Distribution:Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.

Improves Model Fairness:Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.

Why Other Options are Incorrect:

B. Model monitoring for class distribution:Helps identify bias but does not actively correct it.

C. Retrieval Augmented Generation (RAG):Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.

D. Watermark detection for images:Detects watermarks in images, not a technique for addressing bias.

Accuracy is the most appropriate metric to measure the performance of an image classification model. It indicates the percentage of correctly classified images out of the total number of images. In the context of classifying plant diseases from images, accuracy will help the company determine how well the model is performing by showing how many images were correctly classified.

Option B (Correct): "Accuracy":This is the correct answer because accuracy measures the proportion of correct predictions made by the model, which is suitable for evaluating the performance of a classification model.

Option A:"R-squared score" is incorrect as it is used for regression analysis, not classification tasks.

Option C:"Root mean squared error (RMSE)" is incorrect because it is also used for regression tasks to measure prediction errors, not for classification accuracy.

Option D:"Learning rate" is incorrect as it is a hyperparameter for training, not a performance metric.

AWS AI Practitioner References:

Evaluating Machine Learning Models on AWS:AWS documentation emphasizes the use of appropriate metrics, like accuracy, for classification tasks.