A company wants to build a generative AI application by using Amazon Bedrock and needs to choose a foundation model (FM). The company wants to know how much information can fit into one prompt.Which consideration will inform the company's decision?
Answer(s): B
The context window determines how much information can fit into a single prompt. It specifies the number of tokens the foundation model can process at once, affecting the length of input that can be provided. The other options do not directly relate to prompt size.
HOTSPOT (Drag and Drop is not supported)A company needs to customize a base model that is hosted on Amazon Bedrock.Select the correct model customization method from the following list of company requirements. Each model customization method should be selected one or more times.Hot Area:
Answer(s): A
A company has a generative AI model that has limited training data. The model produces output that seems correct but is incorrect.Which option represents the model's problem?
Answer(s): C
Hallucinations occur when a generative AI model produces information that appears plausible or confident but is factually incorrect or unsupported. Limited or insufficient training data can increase the likelihood of this behavior.
A company wants to implement a generative AI solution to improve its marketing operations. The company wants to increase its revenue in the next 6 months.Which approach will meet these requirements?
Using a prebuilt AI assistant allows the company to deploy generative AI quickly without the time and cost of training custom models. This accelerates implementation, enabling measurable business impact such as improved customer engagement and increased revenue within the short 6-month timeframe.
A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.How should the bank fix this issue MOST cost-effectively?
The issue described is a case of bias in the model’s decision-making process, where the model is showing a preference for a specific demographic. The most cost-effective way to address this is to include more diverse training data that better represents all demographics. By fine-tuning the model with this more diverse data,you can help ensure that the model treats all demographic groups fairly and does not exhibit biased behavior.Fine-tuning the model with updated data ensures that the model learns from a more representative sample,improving fairness in its predictions without needing to completely retrain the model from scratch.
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