What is generative AI and why it exploded in AI for Everyone - Interactive Quiz & Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a generative AI model that creates new data.

AI for Everyone

model = [1]()

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AGenerativeModel

BDiscriminativeModel

CRegressionModel

DClusteringModel

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a model that only classifies data instead of generating it.

The generative AI model is designed to create new data similar to the training data. 'GenerativeModel' is the correct choice.

2fill in blank

medium

Complete the code to train a generative AI model on data.

AI for Everyone

model.train([1])

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Alabels

Btest_data

Cvalidation_data

Dtraining_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using test or validation data for training.

Training a generative AI model requires feeding it the training data so it can learn patterns to generate new data.

3fill in blank

hard

Fix the error in the code to generate new data from the model.

AI for Everyone

new_data = model.[1]()

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Apredict

Bfit

Cgenerate

Devaluate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' which is for classification or regression.

To create new data, generative AI models use a 'generate' method, not 'predict' or 'fit'.

4fill in blank

hard

Fill both blanks to create a dictionary of generated samples with their lengths.

AI for Everyone

samples = {sample: len(sample) for sample in model.[1]() if len(sample) [2] 5}

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Agenerate

B>

C<

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' instead of 'generate'.

Using '<' instead of '>' for filtering.

The model generates samples using 'generate'. We filter samples longer than 5 using '>'.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of generated samples with their lengths.

AI for Everyone

filtered = {sample[1]: len(sample) for sample in model.[2]() if len(sample) [3] 10}

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A.upper()

Bgenerate

C>

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' instead of 'generate'.

Not transforming keys to uppercase.

Using '<' instead of '>'.

We transform sample keys to uppercase with '.upper()', generate samples with 'generate()', and filter those longer than 10 with '>'.