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NEW QUESTION # 98
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?
Answer: A
NEW QUESTION # 99
A company is building a contact center application and wants to gain insights from customer conversations.
The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?
Answer: D
Explanation:
Amazon Transcribe is the correct solution for converting audio from customer calls into text, allowing the company to analyze and extract key information from the conversations.
* Amazon Transcribe:
* It is a fully managed automatic speech recognition (ASR) service that converts speech into text, making it easier to perform text-based analysis on audio data.
* After transcribing the audio, further analysis can be performed using other AWS services like Amazon Comprehend to extract insights such as sentiment, key phrases, or entities.
* Why Option B is Correct:
* Conversion to Text: Transcribing audio recordings is the first step in gaining insights from spoken conversations, allowing for further processing.
* Enables Further Analysis: Once the audio is transcribed into text, other tools and services can be used to analyze the content more deeply.
* Why Other Options are Incorrect:
* A. Amazon Lex: Is used for building conversational interfaces, not for transcribing or analyzing audio from customer calls.
* C. Amazon SageMaker Model Monitor: Monitors ML models for bias and data drift, not for audio analysis.
* D. Amazon Comprehend: Can analyze text but cannot transcribe audio; it would be used after transcription is completed.
NEW QUESTION # 100
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?
Answer: D
NEW QUESTION # 101
A company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base.
Which technique should the company use to optimize the generated responses?
Answer: B
Explanation:
The company is building a chatbot using an LLM to answer questions about HR policies, with access to a large digital documentation base. Retrieval Augmented Generation (RAG) optimizes the LLM's responses by retrieving relevant information from the documentation base and using it to generate accurate, contextually grounded answers, reducing hallucinations and improving response quality.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Retrieval Augmented Generation (RAG) enhances the performance of large language models by retrieving relevant information from external knowledge bases, such as documentation or databases, and incorporating it into the generation process. This technique ensures responses are accurate and grounded in the provided data, making it ideal for applications like policy chatbots." (Source: AWS Bedrock User Guide, Retrieval Augmented Generation) Detailed Explanation:
* Option A: Use Retrieval Augmented Generation (RAG).This is the correct answer. RAG leverages the documentation base to provide the LLM with relevant HR policy information, optimizing the chatbot's responses for accuracy and relevance.
* Option B: Use few-shot prompting.Few-shot prompting provides a few examples in the prompt to guide the LLM, but it is less effective than RAG for large documentation bases, as it cannot dynamically retrieve specific policy details.
* Option C: Set the temperature to 1.Setting the temperature to 1 controls the randomness of the LLM' s output but does not optimize responses using external documentation. This option is unrelated to the documentation base.
* Option D: Decrease the token size.Decreasing token size (likely referring to limiting input/output tokens) may reduce response length but does not optimize the quality of responses using the documentation base.
References:
AWS Bedrock User Guide: Retrieval Augmented Generation (https://docs.aws.amazon.com/bedrock/latest
/userguide/rag.html)
AWS AI Practitioner Learning Path: Module on Generative AI Optimization Amazon Bedrock Developer Guide: Building Policy Chatbots (https://aws.amazon.com/bedrock/)
NEW QUESTION # 102
A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.
Which AWS service can the company use to meet this requirement?
Answer: C
NEW QUESTION # 103
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