A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket. The data is encrypted with Amazon S3 managed keys (SSE-S3).The FM encounters a failure when attempting to access the S3 bucket data.Which solution will meet these requirements?
Answer(s): A
The foundation model needs the appropriate permissions to decrypt the encrypted data in the S3 bucket.Ensuring that the role used by Amazon Bedrock has permission to access and decrypt the data will resolve the access failure. The other options are not suitable for addressing the encryption and permission issue.
A company has deployed a generative AI (GenAI) application. The company has observed that identical prompts sometimes produce different responses. The responses occasionally include false information.Which issues does this scenario represent? (Choose two.)
Answer(s): B,C
Nondeterminism means the same prompt can produce different outputs across separate generations.Hallucination occurs when the model generates false, fabricated, or unsupported information.
A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.Which solution will meet these requirements?
Answer(s): B
Increasing the number of epochs allows the model to train for more iterations, improving its accuracy until the model reaches an optimal level. The other options are either less effective or unrelated to improving accuracy.
How can companies use large language models (LLMs) securely on Amazon Bedrock?
Designing clear prompts and using IAM roles with least privilege access ensures secure use of LLMs onAmazon Bedrock by minimizing access risks and preventing misuse. The other options do not directly address securing the use of LLMs.
An AI practitioner needs to improve the accuracy of a natural language generation model. The model uses rapidly changing inventory data.Which technique will improve the model's accuracy?
Answer(s): C
RAG enhances a language model by fetching up-to-date, domain-specific data (e.g., current inventory) at inference time and conditioning the generation on those facts, ensuring the output reflects the latest information and improving accuracy without retraining the core model.
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