A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data. Which type of learning should the company use to train the model?
Correct Answer: A
Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. References: AWS Machine Learning Best Practices.
Question 42
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?
Correct Answer: B
Question 43
A company's large language model (LLM) is experiencing hallucinations. How can the company decrease hallucinations?
Correct Answer: C
Hallucinations in large language models (LLMs) occur when the model generates outputs that are factually incorrect, irrelevant, or not grounded in the input data. To mitigate hallucinations, adjusting the model's inference parameters, particularly the temperature, is a well-documented approach in AWS AI Practitioner resources. The temperature parameter controls the randomness of the model's output. A lower temperature makes the model more deterministic, reducing the likelihood of generating creative but incorrect responses, which are often the cause of hallucinations. Exact Extract from AWS AI Documents: From the AWS documentation on Amazon Bedrock and LLMs: "The temperature parameter controls the randomness of the generated text. Higher values (e.g., 0.8 or above) increase creativity but may lead to less coherent or factually incorrect outputs, while lower values (e.g., 0.2 or 0.3) make the output more focused and deterministic, reducing the likelihood of hallucinations." (Source: AWS Bedrock User Guide, Inference Parameters for Text Generation) Detailed Explanation: * Option A: Set up Agents for Amazon Bedrock to supervise the model training.Agents for Amazon Bedrock are used to automate tasks and integrate LLMs with external tools, not to supervise model training or directly address hallucinations. This option is incorrect as it does not align with the purpose of Agents in Bedrock. * Option B: Use data pre-processing and remove any data that causes hallucinations.While data pre- processing can improve model performance, identifying and removing specific data that causes hallucinations is impractical because hallucinations are often a result of the model's generative process rather than specific problematic data points. This approach is not directly supported by AWS documentation for addressing hallucinations. * Option C: Decrease the temperature inference parameter for the model.This is the correct approach. Lowering the temperature reduces the randomness in the model's output, making it more likely to stick to factual and contextually relevant responses. AWS documentation explicitly mentions adjusting inference parameters like temperature to control output quality and mitigate issues like hallucinations. * Option D: Use a foundation model (FM) that is trained to not hallucinate.No foundation model is explicitly trained to "not hallucinate," as hallucinations are an inherent challenge in LLMs. While some models may be fine-tuned for specific tasks to reduce hallucinations, this is not a standard feature of foundation models available on Amazon Bedrock. References: AWS Bedrock User Guide: Inference Parameters for Text Generation (https://docs.aws.amazon.com/bedrock /latest/userguide/model-parameters.html) AWS AI Practitioner Learning Path: Module on Large Language Models and Inference Configuration Amazon Bedrock Developer Guide: Managing Model Outputs (https://docs.aws.amazon.com/bedrock/latest /devguide/)
Question 44
Which AW5 service makes foundation models (FMs) available to help users build and scale generative AI applications?
Correct Answer: B
Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from various providers, enabling users to build and scale generative AI applications. It simplifies the process of integrating FMs into applications for tasks like text generation, chatbots, and more. Exact Extract from AWS AI Documents: From the AWS Bedrock User Guide: "Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI providers available through a single API, enabling developers to build and scale generative AI applications with ease." (Source: AWS Bedrock User Guide, Introduction to Amazon Bedrock) Detailed Explanation: * Option A: Amazon Q DeveloperAmazon Q Developer is an AI-powered assistant for coding and AWS service guidance, not a service for hosting or providing foundation models. * Option B: Amazon BedrockThis is the correct answer. Amazon Bedrock provides access to foundation models, making it the primary service for building and scaling generative AI applications. * Option C: Amazon KendraAmazon Kendra is an intelligent search service powered by machine learning, not a service for providing foundation models or building generative AI applications. * Option D: Amazon ComprehendAmazon Comprehend is an NLP service for text analysis tasks like sentiment analysis, not for providing foundation models or supporting generative AI. References: AWS Bedrock User Guide: Introduction to Amazon Bedrock (https://docs.aws.amazon.com/bedrock/latest /userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Generative AI Services AWS Documentation: Generative AI on AWS (https://aws.amazon.com/generative-ai/)
Question 45
Which AWS feature records details about ML instance data for governance and reporting?
Correct Answer: A
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.
