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| Section | Objectives |
|---|---|
| Applications of Foundation Models | - Use cases for generative AI in business - AI-powered assistants and automation - Content generation and summarization |
| Fundamentals of Artificial Intelligence and Machine Learning | - Common ML workflows and lifecycle - Data fundamentals for AI/ML - Core AI and ML concepts |
| Fundamentals of Generative AI | - Large language models (LLMs) concepts - Foundation models and prompt engineering basics - AWS generative AI services overview (e.g., Amazon Bedrock) |
| Responsible AI and Security | - Bias, fairness, and explainability - AI ethics and responsible use - Security, privacy, and governance in AI systems |
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NEW QUESTION # 361
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: D
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 Reference:
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 # 362
Which technique involves training AI models on labeled datasets to adapt the models to specific industry terminology and requirements?
Answer: C
Explanation:
Fine-tuning involves training a pre-trained AI model on a labeled dataset specific to a particular task or domain, adapting it to industry terminology and requirements. This process adjusts the model's parameters to better fit the target use case, such as understanding specialized vocabulary or meeting domain-specific needs.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Fine-tuning allows you to adapt a pre-trained foundation model to your specific use case by training it on a labeled dataset. This technique is commonly used to customize models forindustry-specific terminology, improving their accuracy for specialized tasks." (Source: AWS Bedrock User Guide, Model Customization) Detailed Explanation:
* Option A: Data augmentationData augmentation involves generating synthetic data to expand a training dataset, typically for tasks like image or text generation. It does not specifically adapt models to industry terminology or requirements.
* Option B: Fine-tuningThis is the correct answer. Fine-tuning trains a pre-trained model on a labeled dataset tailored to the target domain, enabling it to learn industry-specific terminology and requirements, as described in the question.
* Option C: Model quantizationModel quantization reduces the precision of a model's weights to optimize it for deployment (e.g., on edge devices). It does not involve training on labeled datasets or adapting to industry terminology.
* Option D: Continuous pre-trainingContinuous pre-training extends the initial training of a model on a large, general dataset. While it can improve general performance, it is not specifically tailored to industry requirements using labeled datasets, unlike fine-tuning.
References:
AWS Bedrock User Guide: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide
/custom-models.html)
AWS AI Practitioner Learning Path: Module on Model Training and Customization Amazon SageMaker Developer Guide: Fine-Tuning Models (https://docs.aws.amazon.com/sagemaker/latest
/dg/algos.html)
NEW QUESTION # 363
A company is using Amazon SageMaker to deploy a model that identifies if social media posts contain certain topics. The company needs to show how different input features influence model behavior.
Answer: D
Explanation:
* SageMaker Clarify provides model explainability, bias detection, and helps visualize how input features affect predictions.
* Canvas is for low-code ML building.
* Feature Store is for storing and managing ML features.
* Ground Truth is for data labeling.
# Reference:
AWS Documentation - Amazon SageMaker Clarify
NEW QUESTION # 364
A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.
Which solution meets these requirements?
Answer: D
Explanation:
Amazon SageMaker Model Cards provide a standardized way to document and track model information, including versions and performance. According to AWS documentation:
Explanation:
Amazon SageMaker Model Cards provide a standardized way to document and track model information, including versions and performance. According to AWS documentation:
"SageMaker Model Cards provide a single source of truth for model information including intended use, training details, evaluation metrics, and ethical considerations to support governance and collaboration."
NEW QUESTION # 365
Which THREE of the following principles of responsible AI are most critical to this scenario? (Choose 3)
* Explainability
* Fairness
* Privacy and security
* Robustness
* Safety
Answer:
Explanation:
NEW QUESTION # 366
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