Amazon Web Services AIF-C01 Dumps Questions Answers

To determine customer sentiments based on written customer reviews, the company can use Amazon Comprehend and Amazon Bedrock.

Amazon Comprehend:

A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.

Can analyze customer reviews to detect sentiments (positive, negative, neutral, or mixed) automatically.

Amazon Bedrock:

Provides access to foundational models (FMs) from multiple AI companies for tasks such as text generation, summarization, and sentiment analysis.

The company can use a pre-trained sentiment analysis model available on Amazon Bedrock for processing customer reviews.

Why Other Options are Incorrect:

A. Amazon Lex:Used for building conversational interfaces like chatbots, not for sentiment analysis.

C. Amazon Polly:Converts text to speech; it doesn't analyze sentiment.

E. Amazon Rekognition:Analyzes images and videos, not text.

A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.

Option C (Correct): "Develop a summarization chatbot":This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.

Option A:"Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.

Option B:"Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.

Option D:"Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.

AWS AI Practitioner References:

Using LLMs for Text Summarization on AWS:AWS supports developing summarization tools using its AI services, including Amazon Bedrock.

In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.

Option A (Correct): "Number of tokens consumed":This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.

Option B:"Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.

Option C:"Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.

Option D:"Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.

AWS AI Practitioner References:

Understanding Inference Costs on AWS:AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.