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| Section | Objectives |
|---|---|
| Applications of Foundation Models | - Content generation and summarization - Use cases for generative AI in business - AI-powered assistants and automation |
| Fundamentals of Generative AI | - AWS generative AI services overview (e.g., Amazon Bedrock) - Large language models (LLMs) concepts - Foundation models and prompt engineering basics |
| 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 | - Security, privacy, and governance in AI systems - Bias, fairness, and explainability - AI ethics and responsible use |
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NEW QUESTION # 37
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
Answer: B
NEW QUESTION # 38
Which option is an example of unsupervised learning?
Answer: C
Explanation:
Unsupervised learning involves discovering hidden patterns without labeled data. Example: clustering.
Image recognition (B) is supervised learning.
House price prediction (C) is regression (supervised).
NEW QUESTION # 39
A law firm wants to use Amazon Bedrock foundation models (FMs) to review and negotiate business contracts. The law firm requires customer data and business information to be protected.
Which option is a potential legal risk of this use case?
Answer: B
Explanation:
Incorrect contract analysis represents a significant legal and operational risk when generative AI is used to review or negotiate business agreements.
Foundation models can generate plausible but inaccurate interpretations of contractual language. These errors may result in incorrect obligations, overlooked clauses, misrepresented liabilities, or inappropriate negotiation recommendations.
Such outcomes can expose a law firm and its clients to contractual disputes, financial losses, and potential professional liability.
AWS responsible AI principles emphasize accuracy, reliability, human oversight, and appropriate evaluation of AI-generated outputs, particularly in high-stakes applications.
Option B describes encryption, which protects sensitive information. Option C provides private connectivity through AWS PrivateLink. Option D improves inference performance and potentially reduces costs.
These are protective or optimization mechanisms rather than inherent legal risks.
Therefore, incorrect contract analysis is the correct answer.
NEW QUESTION # 40
An AI practitioner needs to improve the accuracy of a natural language generation model. The model uses rapidly changing inventory data.
Which technique will improve the model's accuracy?
Answer: D
Explanation:
A: Transfer learning: This involves pre-training a model on a large dataset and fine-tuning it for a specific task. While effective for general model improvement, it does not specifically address the challenge of incorporating rapidly changing inventory data into the model's responses.
B: Federated learning: This technique trains models across decentralized devices while keeping data localized, primarily for privacy purposes. It is not designed to handle rapidly changing data or improve NLG model accuracy in this context.
C: Retrieval Augmented Generation (RAG): RAG combines a language model with a retrieval mechanism that fetches relevant, up-to-date information (e.g., inventory data) from an external source during inference. This is ideal for scenarios with dynamic data, as it ensures the model's responses are grounded in the latest information, improving accuracy.
D: One-shot prompting: This involves providing a single example to guide the model's output. While useful for specific tasks, it does not scale well for rapidly changing data or ensure consistent accuracy with dynamic inventory updates.
Exact Extract Reference: According to AWS documentation on generative AI techniques, "Retrieval Augmented Generation (RAG) enhances large language models by retrieving relevant documents or data at inference time, enabling the model to generate accurate and contextually relevant responses, especially for dynamic or frequently updated datasets." (Source: AWS Generative AI Glossary, https://aws.amazon.com/what-is/retrieval-augmented-generation/). This directly addresses the need for accuracy with rapidly changing inventory data.
RAG is the most suitable technique for this scenario, as it allows the model to access and incorporate the latest inventory data, making C the correct answer.
Explanation:
The requirement is to improve the accuracy of a natural language generation (NLG) model that relies on rapidly changing inventory data. Let's evaluate the options:
Reference:
AWS Generative AI Glossary: Retrieval Augmented Generation (https://aws.amazon.com/what-is/retrieval-augmented-generation/) AWS Bedrock Documentation (contextual use of RAG in LLMs) AWS AI Practitioner Study Guide (focus on generative AI techniques for dynamic data)
NEW QUESTION # 41
What is tokenization used for in natural language processing (NLP)?
Answer: D
Explanation:
The verified answer is C. To break text into smaller units for processing. AWS defines tokenization as the process of breaking input text into smaller units called tokens. These tokens can be words, subwords, or characters, and tokenization is used as a preprocessing step for natural language processing tasks. This exactly matches option C.
Tokenization is fundamental because machine learning and generative AI systems do not process raw human language in the same way humans read sentences. Text must first be split into units the model can represent, count, embed, and process mathematically. For example, a sentence might be split into words, subword pieces, punctuation, or characters depending on the tokenizer and model architecture. These tokens can then be converted into numerical representations for downstream tasks such as classification, summarization, question answering, translation, or text generation.
Option A is incorrect because encryption protects data confidentiality by transforming readable text into unreadable ciphertext. Tokenization in NLP is not encryption. The original purpose is linguistic and computational processing, not data protection.
Option B is incorrect because compression reduces file size. Tokenization may reduce text into units, but its purpose is not storage compression. It prepares text for analysis by NLP systems.
Option D is incorrect because translation is a downstream NLP task that converts text from one language to another. Tokenization may be used before translation, but it is not itself translation.
The wording "break text into smaller units" is the defining phrase. Therefore, the correct answer is C.
NEW QUESTION # 42
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