Academy Ai Quiz

Questions covering the core concepts, tools, and real-world scenarios IT professionals encounter in Academy Ai.

This category currently has 60 questions in the SERVBG quiz bank. Below are a few sample questions — the full interactive quiz shuffles through the whole set with instant scoring.

Sample questions

What does a large language model actually read and bill for, instead of whole words?

Whole sentences
Individual ASCII bytes only
Whole paragraphs
Tokens (sub-word units)
Unicode code points, never merged

In a terminal tokenizer demo, why might the word 'internationalization' become two or more tokens instead of one?

The tokenizer only accepts words under 5 letters
Long or uncommon words are often split into smaller sub-word pieces
Every noun is split by definition
Capital letters always force a split
Tokenizers only understand whole dictionary words

What is a 'context window' in the context of an LLM?

The number of turns a user is allowed to type per session
The maximum number of tokens (prompt plus reply) the model can attend to at once
The maximum number of tool calls allowed per request
The physical RAM size of the GPU running the model
The number of documents a RAG system has ingested

A conversation grows past the model's context window. What typically happens to make room for new tokens?

Tokens are compressed losslessly with no information lost
Older messages are truncated or dropped from what gets sent to the model
The model automatically doubles its own context window
The model refuses to respond until the user upgrades hardware
The temperature setting is lowered to compensate

Why does the token count of a prompt usually differ from its plain word count?

Only vowels count as tokens
Tokenizers always produce exactly one token per word
Punctuation, numbers, and rare/long words often split into multiple tokens, while short common words may be a single token
Whitespace is counted as its own separate word every time
Tokens are always exactly 4 characters long

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