| Question.6 A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost. Which solution will meet these requirements? (A) Customize the model by using fine-tuning. (B) Decrease the number of tokens in the prompt. (C) Increase the number of tokens in the prompt. (D) Use Provisioned Throughput. |
6. Click here to View Answer
Correct Answer : B
| Question.7 A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images. Which solution will meet these requirements? (A) Implement moderation APIs. (B) Retrain the model with a general public dataset. (C) Perform model validation. (D) Automate user feedback integration |
7. Click here to View Answer
Correct Answer : A
| Question.8 A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the company take to use the custom model through Amazon Bedrock? (A) Purchase Provisioned Throughput for the custom model. (B) Deploy the custom model in an Amazon SageMaker endpoint for real-time inference. (C) Register the model with the Amazon SageMaker Model Registry. (D) Grant access to the custom model in Amazon Bedrock. |
8. Click here to View Answer
Correct Answer : B
| Question.9 A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company’s product manuals. The manuals are stored as PDF files. Which solution meets these requirements MOST cost-effectively? (A) Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock. (B) Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock. (C) Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts. (D) Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock. |
9. Click here to View Answer
Correct Answer : A
| Question.10 Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team’s VPC? (A) Amazon Personalize (B) Amazon SageMaker JumpStart (C) PartyRock, an Amazon Bedrock Playground (D) Amazon SageMaker endpoints |
10. Click here to View Answer
Correct Answer : D