Week 1 Quiz

What language do computers understand?

• English

• Token

• Binary

• ACII

In our Fish vs Not-Fish activity, what did we learn?

• Generative AI

• Machine Learning

• Deep Learning

• Artificial Intelligence

Order the Algorithm in right steps.
a. Train the Model
b. Test the Model
c. Collect and Label Examples
d. Improve and Retrain

• c a b d

• c d a b

• c a b d

• c b a d

Which statement best defines Generative AI?

• AI designed exclusively for robotics

• AI that analyzes data only to make predictions

• AI that performs only mathematical calculations

• AI that creates new content (text, images, music) by learning patterns

Given the sentence “AI is awesome!”, which tokenization is correct?

• [“AI”, “ is”, “ awesome”, “!”]

• [“AI”, “is”, “awesome”, “!”]

• [“A”, “I”, “ is”, “awesom”, “e”, “!”]

• [“AI”, “ is”, “awes”, “ome”, “!”]

Which of these is NOT a prompt-engineering technique we covered?

• Asking for a specific format or structure (e.g., “List 5 steps…”)

• Specifying the AI’s role (e.g., “You are a friendly teacher…”)

• Using vague, open-ended questions without context

• Setting tone or style (e.g., “Explain like I’m 10 years old…”)

Which description of AGI (Artificial General Intelligence) is correct?

• AI that solves only one narrow task

• AI that can perform any intellectual task a human can

• AI that is already widely used in schools today

• AI that only generates images and art

Match the tool to its primary use-case: which pairing is correct?

• ChatGPT – Generating realistic images

• DALL·E – Writing essays and code

• Gemini – Conversational text and search

• Claude - Translating between languages

To reduce bias in an AI model, you should:

• Include diverse and representative examples in your training data

• Use more complex algorithms without changing the data

• Exclude under-represented groups from training data

• Limit the dataset size to only the quickest-collected samples

In our fish–vs–not-fish activity on Code.org, we used:

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Transfer learning