A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications. Which factor will drive the inference costs?
A. Number of tokens consumed B. Temperature value C. Amount of data used to train the LLM D. Total training time
Answer: A Explanation:In generative AI models, such as those built on Amazon Bedrock, inference costs aredriven by the number of tokens processed. A token can be as short as one character or aslong as one word, and the more tokens consumed during the inference process, the higherthe cost.Option A (Correct): "Number of tokens consumed":This is the correct answerbecause the inference cost is directly related to the number of tokens processedby the model.Option B:"Temperature value" is incorrect as it affects the randomness of themodel's output but not the cost directly.Option C:"Amount of data used to train the LLM" is incorrect because training data
Question # 82
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?
A. Embeddings B. Tokens C. Models D. Binaries
Answer: A Explanation:Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP modelscan easily understand. These representations help models improve their understanding oftextual information by representing concepts in a continuous vector space.Option A (Correct): "Embeddings":This is the correct term, as embeddings providea way for models to learn relationships between different objects in their inputspace, improving their understanding and processing capabilities.Option B:"Tokens" are pieces of text used in processing, but they do not capturesemantic meanings like embeddings do.Option C:"Models" are the algorithms that use embeddings and other inputs, notthe representations themselves.Option D:"Binaries" refer to data represented in binary form, which is unrelated tothe concept of embeddings.AWS AI Practitioner References:Understanding Embeddings in AI and NLP:AWS provides resources and tools, likeAmazon SageMaker, that utilize embeddings to represent data in formats suitablefor machine learning models.
Question # 83
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text. Which type of model meets this requirement?
A. Topic modeling B. Clustering models C. Prescriptive ML models D. BERT-based models
Answer: D Explanation:BERT-based models (Bidirectional Encoder Representations from Transformers) aresuitable for tasks that involve understanding the context of words in a sentence andsuggesting missing words. These models use bidirectional training, which considers thecontext from both directions (left and right of the missing word) to predict the appropriateword to fill in the gaps.BERT-based Models:Why Option D is Correct:Why Other Options are Incorrect:
Question # 84
A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy. Which additional data does the company need to meet these requirements?
A. Pairs of chatbot responses and correct user intents B. Pairs of user messages and correct chatbot responses C. Pairs of user messages and correct user intents D. Pairs of user intents and correct chatbot responses
Answer: C Explanation:Few-shot learning involves providing a model with a few examples (shots) to learn from.For improving intent detection accuracy in a chatbot using a large language model (LLM),the data should consist of pairs of user messages and their corresponding correct intents.Few-shot Learning for Intent Detection:Why Option C is Correct:Why Other Options are Incorrect:
Question # 85
A company is building an application that needs to generate synthetic data that is based on existing data. Which type of model can the company use to meet this requirement?
A. Generative adversarial network (GAN) B. XGBoost C. Residual neural network D. WaveNet
Answer: A Explanation:Generative adversarial networks (GANs) are a type of deep learning model used forgenerating synthetic data based on existing datasets. GANs consist of two neural networks(a generator and a discriminator) that work together to create realistic data.Option A (Correct): "Generative adversarial network (GAN)":This is the correctanswer because GANs are specifically designed for generating synthetic data thatclosely resembles the real data they are trained on.Option B:"XGBoost" is a gradient boosting algorithm for classification andregression tasks, not for generating synthetic data.Option C:"Residual neural network" is primarily used for improving theperformance of deep networks, not for generating synthetic data.Option D:"WaveNet" is a model architecture designed for generating raw audiowaveforms, not synthetic data in general.AWS AI Practitioner References:GANs on AWS for Synthetic Data Generation:AWS supports the use of GANs forcreating synthetic datasets, which can be crucial for applications like trainingmachine learning models in environments where real data is scarce or sensitive.
Question # 86
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?
A. Integration with Amazon S3 for object storage B. Support for geospatial indexing and queries C. Scalable index management and nearest neighbor search capability D. Ability to perform real-time analysis on streaming data
Answer: C Explanation:Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to support vector search, which allows companies to build vector database applications. This is particularly useful in machine learning, where vector representations(embeddings) of data are often used to capture semantic meaning.Scalable index management and nearest neighbor search capabilityare the core features enabling vector database functionalities in OpenSearch. The service allows users to index high-dimensional vectors and perform efficient nearest neighbor searches, whichare crucial for tasks such as recommendation systems, anomaly detection, and semantic search.Here is why option C is the correct answer:Scalable Index Management:OpenSearch Service supports scalable indexing of vector data. This means you can index a large volume of high-dimensional vectors and manage these indexes in a cost-effective and performance-optimized way.The service leverages underlying AWS infrastructure to ensure that indexing scales seamlessly with data size.Nearest Neighbor Search Capability:OpenSearch Service's nearest neighborsearch capability allows for fast and efficient searches over vector data. This isessential for applicationslike product recommendation engines, where the systemneeds to quickly find the most similar items based on a user's query or behavior.AWS AI Practitioner References:The other options do not directly relate to building vector database applications:A. Integration with Amazon S3 for object storageis about storing data objects, notvector-based searching or indexing.B. Support for geospatial indexing and queriesis related to location-based data, notvectors used in machine learning.D. Ability to perform real-time analysis on streaming datarelates to analyzingincoming data streams, which is different from the vector search capabilities.
Question # 87
A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children. Which AWS service or feature will meet these requirements?
A. Amazon Rekognition B. Amazon Bedrock playgrounds C. Guardrails for Amazon Bedrock D. Agents for Amazon Bedrock
Answer: C Explanation:Amazon Bedrock is a service that provides foundational models for building generative AI applications. When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.Option C (Correct): "Guardrails for Amazon Bedrock":This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children’s application, guardrails ensure that all contentgenerated is suitable and appropriate for the intended audience. Option A:"Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos,but it does not handle text or story generation.Option B:"Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specificau diences, such as children. Option D:"Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.AWS AI Practitioner References:Guardrails in Amazon Bedrock:Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.Building Safe AI Applications:AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.
Question # 88
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is notallowed access to any internet traffic.
Which AWS service or feature will meet these requirements?
A. AWS PrivateLink B. Amazon Macie C. Amazon CloudFront D. Internet gateway
Answer: A Explanation:AWS PrivateLink enables private connectivity between VPCs and AWS services withoutexposing traffic to the public internet. This feature is critical for meeting regulatory compliance standards that require isolation from public internet traffic.Option A (Correct): "AWS PrivateLink":This is the correct answer because it allows secure access to Amazon Bedrock and other AWS services from a VPC without internet access, ensuring compliance with regulatory standards. Option B:"Amazon Macie" is incorrect because it is a security service for data classification and protection, not for managing private network traffic.Option C:"Amazon CloudFront" is incorrect because it is a content delivery network service and does not provide private network connectivity.Option D:"Internet gateway" is incorrect as it enables internet access, which violates the VPC's no-internet-traffic policy.AWS AI Practitioner References:AWS PrivateLink Documentation:AWS highlights PrivateLink as a solution for connecting VPCs to AWS services privately, which is essential for organizationswith strict regulatory requirements.