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NEW QUESTION # 168
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning
Answer:
Explanation:
Explanation:
Model fine-tuning adapts a pre-trained model to a specific domain or task using labeled data. This is the preferred approach when you want highly customized behavior for a particular application or subject area.
(Reference: Amazon Bedrock Fine-Tuning)
Data augmentation is used to artificially increase the size of a labeled dataset, usually by transforming or generating variations of the original data. This helps improve model generalization when labeled data is limited.
(Reference: AWS Data Preparation Techniques, AWS AI Practitioner Guide) Continued pre-training (also called domain-adaptive pre-training) further trains a foundation model on large amounts of unlabeled data from a specific domain, improving the model's language understanding or generation for that domain.
(Reference: Amazon Bedrock Customization Options)
NEW QUESTION # 169
A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.
Which AWS service or feature will meet these requirements?
Answer: B
Explanation:
Amazon Bedrock is a service that provides foundational models for building generative AI applications.
When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.
* Option C (Correct): "Guardrails for Amazon Bedrock": This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children's application, guardrails ensure that all content generated is suitable and appropriate for the intended audience.
* Option A: "Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it does not handle text or story generation.
* Option B: "Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specific audiences, such as children.
* Option D: "Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.
AWS AI Practitioner References:
* Guardrails in Amazon Bedrock: Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.
* Building Safe AI Applications: AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.
NEW QUESTION # 170
A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.
Which solution meets these requirements?
Answer: C
Explanation:
The goal is to prevent a fine-tuned large language model (LLM) on Amazon Bedrock from revealing private customer data. Let's analyze the options:
A . Amazon Bedrock Guardrails: Guardrails in Amazon Bedrock allow users to define policies to filter harmful or sensitive content in model inputs and outputs. While useful for real-time content moderation, they do not address the risk of private data being embedded in the model during fine-tuning, as the model could still memorize sensitive information.
B . Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM: Removing PII (e.g., names, addresses, account numbers) from the training dataset ensures that the model does not learn or memorize sensitive customer data, reducing the risk of data leakage. This is a proactive and effective approach to data privacy during model training.
C . Increase the Top-K parameter of the LLM: The Top-K parameter controls the randomness of the model's output by limiting the number of tokens considered during generation. Adjusting this parameter affects output diversity but does not address the privacy of customer data embedded in the model.
D . Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM: Encrypting data in Amazon S3 protects data at rest and in transit, but during fine-tuning, the data is decrypted and used to train the model. If PII is present, the model could still learn and potentially expose it, so encryption alone does not solve the problem.
Exact Extract Reference: AWS emphasizes data privacy in AI/ML workflows, stating, "To protect sensitive data, you can preprocess datasets to remove personally identifiable information (PII) before using them for model training. This reduces the risk of models inadvertently learning or exposing sensitive information." (Source: AWS Best Practices for Responsible AI, https://aws.amazon.com/machine-learning/responsible-ai/). Additionally, the Amazon Bedrock documentation notes that users are responsible for ensuring compliance with data privacy regulations during fine-tuning (https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization.html).
Removing PII before fine-tuning is the most direct and effective way to prevent the model from revealing private customer data, making B the correct answer.
Reference:
AWS Bedrock Documentation: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization.html) AWS Responsible AI Best Practices (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Study Guide (emphasis on data privacy in LLM fine-tuning)
NEW QUESTION # 171
A company is working on a large language model (LLM) and noticed that the LLM's outputs are not as diverse as expected. Which parameter should the company adjust?
Answer: C
Explanation:
The correct answer is A because temperature controls the randomness of a language model ' s output. A higher temperature increases diversity by making the model more likely to explore less probable tokens, while a lower temperature results in more deterministic and repetitive outputs.
From AWS documentation:
" The temperature parameter in LLMs adjusts the randomness of generated responses. Higher values (e.g.,
0.8-1.0) produce more creative and diverse output, while lower values (e.g., 0.1-0.3) make output more focused and repetitive. " Explanation of other options:
B). Batch size is related to training efficiency, not output diversity.
C). Learning rate affects the training convergence rate, not inference-time output variety.
D). Optimizer type is a training configuration that influences how the model learns during training, not diversity during inference.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock - Parameter Tuning Guide
AWS Machine Learning Specialty Guide - LLM Inference Parameters
NEW QUESTION # 172
A company is testing the security of a foundation model (FM). During testing, the company wants to get around the safety features and make harmful content.
Answer: C
Explanation:
Comprehensive and Detailed
Jailbreaking an LLM refers to deliberately crafting prompts to bypass its safety guardrails and produce restricted or harmful content.
Fuzzing is input testing for software.
DoS is a service availability attack.
Pen testing (C) is allowed with AWS authorization but doesn't describe bypassing model safeguards.
Reference:
AWS Responsible AI - Prompt Injection and Jailbreak Risks
NEW QUESTION # 173
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