Exam Questions 1 Flashcards
What is the difference between model evaluation and inferencing ?
Model evaluation is the process of evaluating and comparing model outputs to determine the model that is best suited for a use case, whereas, model inference is the process of a model generating an output (response) from a given input (prompt)
What is this concept called that defines the maximum amount of text or characters the AI model can process at one time?
This concept is referred to as a context window, which determines the amount of text or information the model can consider at once while generating a response, typically measured in tokens rather than characters
Which is the default vector database supported by Knowledge Bases for Amazon Bedrock?
OpenSearch Serverless vector store
A company needs large, high-quality, and labeled datasets for training its machine learning models. Which Amazon SageMaker service helps build high-quality training datasets?
Ground Truth built-in task types to have workers generate specific types of labels for your data. You can also build a custom labeling workflow to provide your UI and tools to workers labeling your data
What is the effect of increasing the number of epochs ?
Increasing the number of epochs allows the model to learn from the training data for a longer period, potentially capturing more complex patterns and relationships, which can improve accuracy. Multiple epochs are run until the accuracy of the model reaches an acceptable level, or when the error rate drops below an acceptable level
What are the differences between Bedrock and Amazon Q ?
Amazon Q is a generative AI–powered assistant that allows you to create pre-packaged generative AI applications, whereas, Amazon Bedrock provides an environment to build and scale generative AI applications using a Foundation Model (FM)
With Amazon Bedrock, you can choose the underlying Foundation Model. However, Amazon Q does not allow you to choose the underlying Foundation Model
What is transfer learning ?
The company should use transfer learning, a method where a model pre-trained on one task is adapted to improve performance on a different but related task by leveraging knowledge from the original task
What is a key difference between Foundation Models (FMs) and Large Language Models (LLMs) in the context of generative AI?
Foundation Models serve as a broad base for various AI applications by providing generalized capabilities, whereas Large Language Models are specialized for understanding and generating human language
What AWS services does sentiment analysis ?
Transcribe and Comprehend
What setting in bedrock can determine the creativity of a response ?
Temperature higher the more creative
How do transformer models work ?
Transformer models use a self-attention mechanism and implement contextual embeddings
Transformer models are a type of neural network architecture designed to handle sequential data, such as language, in an efficient and scalable way. They rely on a mechanism called self-attention to process input data, allowing them to understand and generate language effectively. Self-attention allows the model to weigh the importance of different words in a sentence when encoding a particular word. This helps the model capture relationships and dependencies between words, regardless of their position in the sequence.
Transformer models use self-attention to weigh the importance of different words in a sentence, allowing them to capture complex dependencies. Positional encodings provide information about word order, and the encoder-decoder architecture enables effective processing and generation of sequences. This makes transformers highly effective for tasks like language translation, text generation, and more.
What is the most effective approach to implement this access control and maintain data security in Amazon Bedrock?
The company should create a service role for Amazon Bedrock for each team, granting access only to the specific team’s clients data in Amazon S3
Is a decision tree supervised or unsupervised
Supervised
In order of complexity what are the best ways to improve a models answers ?
Prompt engineering, Retrieval Augmented Generation (RAG), Fine-tuning
What are the differences between Retrieval augmented generation (RAG) and Agent in the context of Amazon Bedrock?
RAG refers to querying and retrieving information from a data source to augment a generated response to a prompt, whereas, Agent refers to an application that carries out orchestrations through cyclically interpreting inputs and producing outputs by using a foundation model
What performance metrics would you recommend to the team for evaluating the effectiveness of its classification system?
Precision, Recall and F1-Score
What type of data sets should I use to detect bias ?
The company should use benchmark datasets, which are pre-compiled, standardized datasets specifically designed to test for biases and discrimination in model outputs
In Amazon Q Business what are the sources of model responses ?
Amazon Q Business chat responses can be generated using model knowledge and enterprise data, or enterprise data only
What controls are in Amazon Q Business ?
Amazon Q Business guardrails support topic-specific controls to determine the web application environment’s behavior when it encounters a mention of a blocked topic by an end-user
Whats the difference between feature extraction and feature selection ?
Feature extraction reduces the number of features by transforming data into a new space, while feature selection reduces the number of features by selecting the most relevant ones from the existing features
What is Top K
Influences the number of most-likely candidates that the model considers for the next token from the pool of candidates. Lowering the value means the model only considers the most likely or conservative answers.
What is Top P
Influences the size of the pool that top k operates on. Lower value decreases size of the pool. Remember it is a percentage - rather than a fixed number.
When does overfitting occur ?
Overfitting occurs when the model is overly complex and captures noise or random fluctuations in the training data rather than the underlying patterns
Whats the difference between CNNs and RNNs ?
While CNNs are used for single image analysis, RNNs are used for video analysis
