What are the categories of inference parameters in AI models? Input and output Speed and accuracy Randomness and diversity, Length Memory and storage

Randomness and diversity, Length

Under randomness and diversity, what is the temperature parameter in AI models? It determines the speed of processing. It controls the input data size. It influences how creative or predictable the model's output will be. It sets the memory usage limit.

It influences how creative or predictable the model's output will be.

What is the Top K inference parameter in AI models? The maximum number of iterations allowed. The number of most likely candidates that the model considers for the next token. The threshold for error tolerance. The percentage of data processed at once.

The number of most likely candidates that the model considers for the next token.

What is the Top P inference parameter in AI models? The size of the input data batch. The number of layers in the model. The percentage of most likely candidates that the model considers for the next token. The maximum processing time allowed.

The percentage of most likely candidates that the model considers for the next token.

What is classification for prompt engineering use cases? The model must respond with the correct choice from several possible answers. The model generates a summary of the input text. The model predicts future data trends. The model translates text from one language to another.

The model must respond with the correct choice from several possible answers.

What is question-answer without context for prompt engineering use cases? The model provides a detailed explanation of the question. The model must answer the question with its internal knowledge without any context or document. The model uses external documents to find the answer. The model generates a list of possible answers.

The model must answer the question with its internal knowledge without any context or document.

What is question-answer with context for prompt engineering use cases? The model ignores the input text and answers based on its training. The user provides an input text with a question, and the model must answer based on the input text. The model generates a random answer. The model uses a predefined set of answers.

The user provides an input text with a question, and the model must answer based on the input text.

What is summarization for prompt engineering use cases? The model translates the input text into another language. The model responds with a shorter passage that captures the main points of the input. The model predicts the next sentence in the input text. The model generates a list of keywords from the input.

The model responds with a shorter passage that captures the main points of the input.

What is open-ended text generation for prompt engineering use cases? The model generates a summary of the input text. The model predicts the next word in the input text. The model responds with a passage of original text that matches the description. The model translates the input text into another language.

The model responds with a passage of original text that matches the description.

What is code generation for prompt engineering use cases? The model optimizes existing code for performance. The model predicts the next line of code in a script. The model translates code from one language to another. The model generates code based on user specifications, such as text-to-SQL or text-to-Python.

The model generates code based on user specifications, such as text-to-SQL or text-to-Python.

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Computer Science - Software Engineering

user_kumartyv Created by 7 mon ago

Cards in this deck(100)

What are the categories of inference parameters in AI models?

Under randomness and diversity, what is the temperature parameter in AI models?

What is the Top K inference parameter in AI models?

What is the Top P inference parameter in AI models?

What is classification for prompt engineering use cases?

What is question-answer without context for prompt engineering use cases?

What is question-answer with context for prompt engineering use cases?

What is summarization for prompt engineering use cases?

What is open-ended text generation for prompt engineering use cases?

What is code generation for prompt engineering use cases?

What is mathematics for prompt engineering use cases?

What is reasoning or logical thinking for prompt engineering use cases?

What AWS SageMaker feature can help you harness the power of human feedback across the ML lifecycle?

What is data preparation in ML?

What type of data is a text file that uses JSON format?

What are the two major components of a deployable ML model?

When is batch a suitable inferencing option to deploy an ML model?

What is a likely cause of model overfitting?

How can an ML model become biased?

What ML problem type uses labeled training data and target values that are mathematically continuous?

What is meant by a model's interpretability?

Why is AI good at recognizing fraud?

Which AWS AI service helps companies build conversational voice and text interfaces to engage with customers?

Which AWS AI service could you use to filter uploaded images that contain inappropriate content?

What is the first stage of an ML pipeline?

What is the primary purpose of the AWS Glue Data Catalog?

Which type of SageMaker inference is most cost-effective for real-time inference when there are also periods of no traffic or intermittent traffic?

Which model performance metric attempts to balance the optimization of both precision and recall?

In a confusion matrix, what is the definition of a true negative?

What is generative AI?

What is a prompt in AI models?

What is inference in AI models?

What is a vector in the context of AI and ML?

Which AWS services and tools can be used for building summarization and translation applications?

What are examples of generative AI applications that can be used at an enterprise level?

What are generative AI use cases for corporate human resources (HR) and recruiting?

What are generative AI use cases for corporate sales and marketing?

What are generative AI use cases for corporate customer service?

Which AWS services and tools can help build search and recommendation applications with generative AI?

What are AWS tools for data and model selection in the FM lifecycle?

What is a sequence-to-sequence model in AI?

What are considerations for choosing the best LLM?

What are recurrent neural networks (RNNs)?

What are transformer-based models?

What are some potential consequences to a model's performance if there is class imbalance in the training dataset?

Which AWS service runs processing jobs that analyze both training data and inference endpoints for bias and explainability?

What is the primary cause for generative AI models to sometimes hallucinate?

How can you prevent a foundation model (FM) in Amazon Bedrock from commenting on politics?

What are two tradeoffs to consider when choosing a model with high interpretability?

Why do some companies discourage employees from using open source AI models?

How does a partial dependence plot help explain a model's outputs?

What learning technique in Amazon Augmented AI (Amazon A2I) rewards a model for responses that are more preferred by humans?

Which AWS service can send low-confidence predictions to humans for review?

What bias metric indicates if there are more true positive predictions for a preferred class?

Under the Shared Responsibility Model, who is responsible for creating the IAM policies to grant access to an S3 bucket?

Why is it important to secure the credentials for the root user for an AWS account?

How can customers be alerted to the presence of personally identifiable information (PII) in their S3 buckets?

What is model inversion?

How could you reduce the susceptibility of a model to adversarial input?

Where can you obtain compliance reports produced by third-party auditors regarding security controls maintained by AWS?

How can you block a generative AI model from commenting on a particular religion?

What is the difference between a data steward and a data owner?

How can training data in Amazon S3 be automatically moved into archival storage after 30 days?

How can using a pre-trained model instead of a self-trained AI model for your solution streamline your AI governance strategy?

What is Artificial Intelligence?

What is Conversational AI?

What is an Embedding in AI?

What is Supervised Learning?

What is Classification in AI?

What is Regression in AI?

What is Unsupervised Learning?

What is Clustering in AI?

What is Dimensionality Reduction in AI?

What is Reinforcement Learning?

What is Intelligent Document Processing (IDP)?

What is Adaptability in the context of generative AI?

What is Responsiveness in the context of generative AI?

What is Simplicity in the context of generative AI?

What is Creativity and Exploration in the context of generative AI?

What is Data Efficiency in the context of generative AI?

What is Scalability in the context of generative AI?

What are factors to consider when selecting a generative AI model?

What is Amazon Q Business?

What is Responsible AI?

What are Traditional Machine Learning Models?

What are challenges of generative AI?

What are Multimodal Models?

What is Amazon SageMaker?

What is Machine Learning?

What is Natural Language Processing (NLP)?

What is Amazon SageMaker Clarify?

What is Deep Learning?

What are Neural Networks?

What is Computer Vision?

What is Amazon SageMaker Feature Store?

What is Amazon SageMaker Model Monitor?

What is Fine-tuning in AI?

What is Amazon SageMaker JumpStart?

What is Amazon Bedrock?

What is PartyRock in Amazon Bedrock?

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