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| Section | Objectives |
|---|---|
| Applications of Foundation Models | - Content generation and summarization - AI-powered assistants and automation - Use cases for generative AI in business |
| Fundamentals of Artificial Intelligence and Machine Learning | - Common ML workflows and lifecycle - Data fundamentals for AI/ML - Core AI and ML concepts |
| Responsible AI and Security | - AI ethics and responsible use - Security, privacy, and governance in AI systems - Bias, fairness, and explainability |
| Fundamentals of Generative AI | - Foundation models and prompt engineering basics - Large language models (LLMs) concepts - AWS generative AI services overview (e.g., Amazon Bedrock) |
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NEW QUESTION # 233
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model
Answer:
Explanation:
Reference:
Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.
NEW QUESTION # 234
A company wants to develop a solution that uses generative AI to create content for product advertisements, Including sample images and slogans.
Select the correct model type from the following list for each action. Each model type should be selected one time. (Select THREE.)
* Diffusion model
* Object detection model
* Transformer-based model
Answer:
Explanation:
Explanation:
Diffusion models are state-of-the-art generative models for creating high-quality, realistic images from textual prompts or other forms of conditioning. These are the foundational technology behind tools like Amazon Bedrock Titan Image Generator and other generative image models.
Reference: AWS Generative AI Overview, Diffusion Models Explained - AWS Blog Transformer-based models (such as GPT or Amazon Titan Text) are designed for generating and understanding natural language. These models can generate coherent, contextually relevant slogans based on product information.
Reference: AWS Generative AI on Bedrock, Transformers Explained - AWS
Object detection models are designed to identify and locate objects within images, which makes them suitable for verifying that specific brand elements (like logos or products) are correctly positioned in the generated content.
Reference: AWS Rekognition Object Detection, Object Detection Overview - AWS
NEW QUESTION # 235
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.)
Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning
Answer:
Explanation:
Explanation:
AWS References:
Amazon Bedrock - Retrieval Augmented Generation (RAG)
AWS Generative AI Guide - Zero-shot & Few-shot learning
NEW QUESTION # 236
A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?
Answer: C
Explanation:
Reinforcement learning allows a model to learn and improve over time based on feedback from its environment. In this case, the chatbot can improve its responses by being rewarded for positive customer feedback, which aligns well with the goal of self-improvement based on past interactions and new information.
* Option B (Correct): "Reinforcement learning with rewards for positive customer feedback": This is the correct answer as reinforcement learning enables the chatbot to learn from feedback and adapt its behavior accordingly, providing self-improvement capabilities.
* Option A: "Supervised learning with a manually curated dataset" is incorrect because it does not support continuous learning from new interactions.
* Option C: "Unsupervised learning to find clusters of similar customer inquiries" is incorrect because unsupervised learning does not provide a mechanism for improving responses based on feedback.
* Option D: "Supervised learning with a continuously updated FAQ database" is incorrect because it still relies on manually curated data rather than self-improvement from feedback.
AWS AI Practitioner References:
* Reinforcement Learning on AWS: AWS provides reinforcement learning frameworks that can be used to train models to improve their performance based on feedback.
NEW QUESTION # 237
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?
Answer: C
Explanation:
When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model.
Explanation of Each Option:
Option A (Correct): "Delete the custom model. Remove the confidential data from the training dataset.
Retrain the custom model."This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock.
Option B: "Mask the confidential data in the inference responses by using dynamic data masking."This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database.
It does not address the core problem of the model beingtrained on confidential data. Masking data in inference responses does not prevent the model from using confidential data it learned during training.
Option C: "Encrypt the confidential data in the inference responses by using Amazon SageMaker."This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model's underlying knowledge or training process.
Option D: "Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS)."This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training. AWS KMS can encrypt data, but it does not modify the learning that the model has already performed.
AWS AI Practitioner References:
Data Handling Best Practices in AWS Machine Learning: AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models.
Amazon Bedrock and Model Training Security: Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.
NEW QUESTION # 238
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