What is Generative AI?

RAG Model

Quiz
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Specialty
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University
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Easy
paula o.n.
Used 2+ times
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
An AI system that only analyzes existing data without producing new content.
An AI system that creates new content such as text, images, and music based on learned patterns.
A rule-based AI system that requires explicit programming for every task.
An AI system exclusively designed for analyzing numerical data
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key difference between a RAG model and an LLM?
RAG models integrate external knowledge retrieval with language generation, while LLMs rely only on pre-trained knowledge.
RAG models are smaller and less efficient than LLMs.
LLMs require external databases to function, while RAG models do not.
RAG models are designed only for text summarization tasks.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is NOT a type of chunking?
Fixed-Size Chunking.
Semantic Chunking.
Recursive Chunking.
Dynamic Vector Chunking.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Given the text divided as follows, what type of chunking is used?
Chunk 1: "Cats are popular pets."
Chunk 2: "Dogs are loyal companions."
Chunk 3: "Birds can make excellent pets too."
Fixed-Size Chunking
Semantic Chunking
Recursive Chunking
Randomized Chunking
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which pair of words is likely to have the least cosine similarity in their embeddings?
Artificial Intelligence and Machine Learning.
Cat and Dog.
Artificial Intelligence and Swimming.
Mathematics and Calculus.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What type of database is commonly used in RAG models?
Relational Database.
Vector Database.
Graph Database.
Time-Series Database.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary function of a RAG model?
To generate outputs solely based on its training data.
To retrieve relevant information from external sources and generate context-aware responses.
To classify and tag data points based on pre-defined categories.
To enhance computational efficiency in multi-modal data processing.
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