AWS Certified AI Practitioner (AIF-C01)

The AWS Certified AI Practitioner (AIF-C01) were last updated on today.

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Viewing questions 41-45 out of 120 questions

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Topic 1 - Exam A

Question #41 Topic 1

A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy. Which additional data does the company need to meet these requirements?

A Pairs of chatbot responses and correct user intents
B Pairs of user messages and correct chatbot responses
C Pairs of user messages and correct user intents
D Pairs of user intents and correct chatbot responses

Suggested Answer: C
NOTE: 为了提高意图检测的准确性，公司需要使用少样本学习（few-shot learning）。在这种情况下，公司需要用户提供消息及其对应的正确用户意图。因为少样本学习依赖于示例对，这些示例对需要包括用户的输入（消息）和期望的输出（意图），从而帮助模型理解用户意图。

Question #42 Topic 1

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?

A Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
B Mask the confidential data in the inference responses by using dynamic data masking.
C Encrypt the confidential data in the inference responses by using Amazon SageMaker.
D Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).

Suggested Answer: B
NOTE: 选项 B 是正确的，因为它直接解决了防止模型基于保密数据生成推理响应的问题。通过使用动态数据屏蔽来遮盖推理响应中的保密数据，可以确保即使模型接触到保密数据，最终的输出也不会泄露这些数据。

Question #43 Topic 1

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?

A Create a prompt template that teaches the LLM to detect attack patterns.
B Increase the temperature parameter on invocation requests to the LLM.
C Avoid using LLMs that are not listed in Amazon SageMaker.
D Decrease the number of input tokens on invocations of the LLM.

Suggested Answer: A
NOTE: 选项 A 是正确的，因为它通过创建一个提示模板来教导 LLM 检测攻击模式，从而减少风险。这有助于识别和防止恶意输入。选项 B、C 和 D 都不是最佳选择：增加温度参数（B）可能会导致模型输出更多变的结果，而不是减少风险；仅使用 Amazon SageMaker 列出的 LLM（C）并不能确保安全性，因为攻击可能仍然存在；减少输入标记的数量（D）可能会限制模型的理解能力，但不会直接防止攻击。

Question #44 Topic 1

A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone. Which solution meets these requirements?

A Set a low limit on the number of tokens the FM can produce.
B Use batch inferencing to process detailed responses.
C Experiment and refine the prompt until the FM produces the desired responses.
D Define a higher number for the temperature parameter.

Suggested Answer: C
NOTE: 选项 C 是正确答案，因为通过实验和优化提示可以确保基础模型（FM）生成符合公司语气的响应。这符合要求中提到的需要在没有人工干预的情况下解决技术问题，并且生成的响应需要符合公司的语气。

Question #45 Topic 1

Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?

A Integration with Amazon S3 for object storage
B Support for geospatial indexing and queries
C Scalable index management and nearest neighbor search capability
D Ability to perform real-time analysis on streaming data

Suggested Answer: C
NOTE: The question asks about the feature of Amazon OpenSearch Service that enables companies to build vector database applications. The correct answer is C because it mentions 'Scalable index management and nearest neighbor search capability', which is essential for building vector database applications.