A company is monitoring a predictive model by using Amazon SageMaker Model Monitor. The company notices data drift beyond a defined threshold. The company wants to mitigate a potentially adverse impact on the predictive model.Which solution will meet these requirements?
Answer(s): C
When Amazon SageMaker Model Monitor detects data drift beyond a defined threshold, it indicates that the current data differs from what the model was trained on. Retraining the model with recent data ensures it adapts to new patterns and maintains predictive accuracy.
What is continues pre-training?
Answer(s): B
Continued pre-training improves a pre-trained language model’s domain knowledge by training it further on large volumes of unlabeled, domain-specific data, enabling better understanding and performance in that specific domain.
A company is using custom models in Amazon Bedrock for a generative AI application. The company wants to use a company managed encryption key to encrypt the model artifacts that the model customization jobs create.Which AWS service meets these requirements?
Answer(s): A
AWS Key Management Service (AWS KMS) is a fully managed service that allows you to create and control encryption keys used to encrypt your data. It is ideal for scenarios like this, where a company wants to use a custom, company-managed encryption key to protect model artifacts. AWS KMS allows you to securely manage keys for encrypting and decrypting data, and it integrates with various AWS services, includingAmazon Bedrock.AWS KMS provides centralized key management, and it is designed for use cases that involve encrypting both data and artifacts, such as model customization jobs.
A social media company uses an AI-based "Add as Friend" recommendation system. The company wants to ensure that its AI models are transparent and explainable.Which solution will meet these requirements in the MOST operationally efficient way?
Amazon SageMaker Clarify helps improve model transparency and explainability by showing how input features contribute to model predictions. This supports understanding why the recommendation system makes specific “Add as Friend” recommendations.
Which metric is used to evaluate the performance of foundation models (FMs) for text summarization tasks?
Text summarization quality is most commonly assessed using ROUGE metrics, which report recall, precision,and an F1-score that balances the two. The ROUGE-F1 (often simply called the F1 score in this context)measures how well the model’s summary overlaps with reference summaries, making it the standard choice for evaluating foundation models on summarization tasks.
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