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A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt. Which consideration will inform the company's decision?
A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information. Which action will reduce these risks?
An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance. Which metric will help the AI practitioner evaluate the performance of the model?
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements. Which solution meets these requirements?
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model. Which technique will solve the problem?
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources. Which solution will meet this requirement?
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?
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output. Which ML algorithm meets these requirements?
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data. Which strategy will successfully fine-tune the model?
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data. How should the AI practitioner prevent responses based on confidential data?

Question 20 - AIF-C01 discussion

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A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.

Which solution will meet these requirements?

A.
Configure the security and compliance by using Amazon Inspector.
Answers
A.
Configure the security and compliance by using Amazon Inspector.
B.
Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
Answers
B.
Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
C.
Encrypt and secure training data by using Amazon Macie.
Answers
C.
Encrypt and secure training data by using Amazon Macie.
D.
Gather more data. Use Amazon Rekognition to add custom labels to the data.
Answers
D.
Gather more data. Use Amazon Rekognition to add custom labels to the data.
Suggested answer: B
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asked 16/09/2024
Rafal Wozniak
37 questions
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