A company wants to develop an AI assistant for employees to query internal data.Which AWS service will meet this requirement?
Answer(s): D
Amazon Q Business is designed to build generative AI assistants for querying and interacting with internal organizational data, making it the ideal service for creating an AI assistant for employees to access company information.
HOTSPOT (Drag and Drop is not supported)A company wants more customized responses to its generative AI models’ prompts.Select the correct customization methodology from the following list for each use case. Each use case should be selected one time.Hot Area:
Answer(s): A
The models must be taught a new domain-specific task - Model fine-tuningFine-tuning is the best approach when a model needs to learn a specific domain-related task. It involves adjusting a pre-trained model with a smaller, task-specific dataset to improve performance in that domain.A limited amount of labeled data is available and more data is needed - Data augmentationData augmentation helps in scenarios where labeled data is scarce by artificially increasing the size and variability of the dataset, improving model generalization.Only unlabeled data is available - Continued pre-trainingContinued pre-training allows an existing foundation model to be further trained on domain-specific, unlabeled data to adapt to new contexts without requiring labeled data.
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.Which Amazon Bedrock pricing model meets these requirements?
The On-Demand pricing model provides flexibility without requiring a long-term commitment, allowing the company to pay only for the resources used, which fits well with a limited budget. The other options are either not relevant to pricing flexibility or involve specific resource commitments.
A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data.Which type of learning should the company use to train the model?
In supervised learning, the model is trained using a labeled dataset, where each image (input) has a corresponding label (the type of animal, in this case). Since the company already has a large dataset of labeled images, supervised learning is the most appropriate approach. The model learns to classify the images based on the features and labels in the training data.
A real estate company is developing an ML model to predict house prices by using sales and marketing data.The company wants to use feature engineering to build a model that makes accurate predictions.Which approach will meet these requirements?
Answer(s): C
Feature engineering focuses on creating, transforming, and selecting the most relevant input variables for model training, which directly improves the model’s ability to learn meaningful patterns and make accurate house price predictions.
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