Intermediate result handling in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to store the intermediate result in a variable.

Agentic AI

intermediate_result = model.predict([1])

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Atest_data

Boutput_data

Cinput_data

Dtrain_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using output_data instead of input_data

Passing train_data instead of input_data

The model predicts using the input data, so input_data is the correct variable to pass.

2fill in blank

medium

Complete the code to calculate the accuracy of the model predictions.

Agentic AI

accuracy = accuracy_score([1], predictions)

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Atest_data

Bpredictions

Cinput_data

Dtrue_labels

Attempts:

3 left

💡 Hint

Common Mistakes

Using predictions as both arguments

Using input_data instead of true_labels

Accuracy compares the true labels with the predictions, so true_labels is needed.

3fill in blank

hard

Fix the error in the code to correctly update the intermediate result.

Agentic AI

intermediate_result = intermediate_result [1] new_data

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A+

B*

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication instead of addition

Using subtraction which changes the result incorrectly

To update the intermediate result by adding new data, use the plus operator +.

4fill in blank

hard

Fill both blanks to filter and transform intermediate results correctly.

Agentic AI

filtered_results = [x[1]2 for x in results if x [2] 5]

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A**

B%

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using modulus operator instead of power

Filtering with less than instead of greater than

We square each x using **2 and keep only those greater than 5 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of filtered intermediate results.

Agentic AI

result_dict = { [1]: [2] for [3] in data if [2] > 0 }

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Ak

Bv

Ck, v

Attempts:

3 left

💡 Hint

Common Mistakes

Using tuple unpacking incorrectly

Using the same variable for key and value

The dictionary comprehension uses k as key and v as value, iterating over k, v in data. The condition filters values greater than zero.