🎯 LLM Fundamentals Quiz

⏱️ 30 min 📚 Quiz
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Question 1 of 14

Which statement about large language models (LLMs) is true?

A
LLMs must be trained with labeled answers for each sentence
B
LLMs learn language patterns by reading large amounts of text without requiring labeled answers
C
LLMs understand text exactly like a human does
D
LLMs can only handle one specific task and are not versatile

Question 2 of 14

LLM stands for Large _______ Model.

💡 Drag or click the correct word from below into the blank to complete the sentence.
LLM stands for Large
Model.
Language
Learning
Logic
Linear

Question 3 of 14

In LLM training, what does self-supervised learning mean?

A
The model is given correct answers by humans for every input
B
The model uses trial-and-error feedback (reinforcement) to learn
C
The model only learns if the data is labeled by humans
D
The model learns by predicting missing or next words without human labels

Question 4 of 14

During training, an LLM often tries to predict the next ______ in a sentence.

💡 Drag or click the correct word from below into the blank to complete the sentence.
During training, an LLM often tries to predict the next
in a sentence.
sentence
paragraph
word
letter

Question 5 of 14

In the context of LLMs, what is a token?

A
A word or part of a word that the model processes
B
A single letter in the text
C
An entire paragraph
D
A type of neural network layer

Question 6 of 14

Converting raw text into tokens is called ______.

💡 Drag or click the correct word from below into the blank to complete the sentence.
Converting raw text into tokens is called
embedding
encoding
parsing
tokenization

Question 7 of 14

What feature of transformer models helps them understand context across an entire sentence?

A
Fixed word embeddings
B
Self-attention (focusing on relevant words)
C
Convolutional filters
D
Manual grammar rules

Question 8 of 14

Transformers can process tokens _____ at once, unlike older models that read one word at a time.

💡 Drag or click the correct phrase from below into the blank to complete the sentence.
Transformers can process tokens
at once, unlike older models that read one word at a time.
in parallel
sequentially
randomly
individually

Question 9 of 14

What does it mean if an LLM has an emergent ability?

A
It gradually improves at a task as training continues
B
It only works if explicitly programmed to do that task
C
It suddenly gains a new skill only when the model is large enough
D
It means the model failed at the task

Question 10 of 14

An emergent ability often appears only after a model reaches a certain ______.

💡 Drag or click the correct word from below into the blank to complete the sentence.
An emergent ability often appears only after a model reaches a certain
speed
scale
accuracy
temperature

Question 11 of 14

How do LLMs differ from traditional AI programs?

A
LLMs follow fixed rules and match exact keywords
B
LLMs learn meaning and context, not just keywords.
C
LLMs can only perform one task at a time
D
LLMs require less training data than traditional AI

Question 12 of 14

What is the context window of an LLM?

A
The total number of parameters in the model
B
The speed at which it processes text
C
The number of training examples used
D
The limit on how many tokens it can consider at once

Question 13 of 14

Why do transformers use positional encodings?

A
To understand word order since self-attention alone doesn't know position
B
To make the model faster
C
To reduce the number of parameters
D
To predict the next word more accurately

Question 14 of 14

What is true about training large language models?

A
Training needs huge computing power and can cost millions of dollars
B
Training requires minimal computing power and can be done on a laptop
C
Training takes only a few hours
D
Training doesn't require any data preprocessing

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