AI for data analysis and spreadsheet tasks in AI for Everyone - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How AI assists in data cleaning in spreadsheets

Which of the following best describes how AI can help clean data in spreadsheets?

A AI can automatically detect and correct inconsistent data entries by learning patterns in the data.

B AI replaces all manual data entry by generating random data to fill empty cells.

C AI only highlights errors but cannot suggest corrections or fix them.

D AI deletes all duplicate rows without checking if they are truly duplicates.

💡 Hint

Think about how AI learns from data patterns to improve quality.

AI uses pattern recognition to find inconsistencies and can suggest or apply corrections, improving data quality automatically.

🔍 Analysis

intermediate

Output of AI-generated summary for spreadsheet data

Given a spreadsheet with sales data, an AI tool generates this summary code snippet. What is the output?

AI for Everyone

sales = [120, 150, 100, 130, 170]
summary = {'total_sales': sum(sales), 'average_sales': sum(sales)/len(sales)}
print(summary)

A {'total_sales': 670, 'average_sales': 135.0}

B {'total_sales': 670, 'average_sales': 134.0}

C {'total_sales': 650, 'average_sales': 130.0}

D TypeError: unsupported operand type(s) for +: 'int' and 'list'

💡 Hint

Calculate the sum and average of the list values carefully.

The sum of the sales list is 670, and the average is 670 divided by 5, which is 134.0.

❓ data_output

advanced

Result of AI clustering on spreadsheet data

An AI model clusters customer data into groups based on spending. Given this code, what is the cluster assignment output?

AI for Everyone

import numpy as np
from sklearn.cluster import KMeans

X = np.array([[100, 200], [110, 210], [500, 600], [520, 610]])
model = KMeans(n_clusters=2, random_state=0).fit(X)
print(model.labels_)

A [0 0 1 1]

B [1 1 0 0]

C [0 1 0 1]

D ValueError: n_clusters must be less than or equal to the number of samples

💡 Hint

Look at how the points are grouped by proximity.

The first two points are close and assigned cluster 0, the last two are close and assigned cluster 1.

🔍 Analysis

advanced

Identify the error in AI data extraction code

What error does this AI-powered spreadsheet data extraction code produce?

AI for Everyone

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
ages = data['age']
print(ages)

A TypeError: list indices must be integers or slices

B Output: [25, 30]

C NameError: name 'data' is not defined

D KeyError: 'age'

💡 Hint

Check the exact spelling and case of dictionary keys.

The key 'age' does not exist because dictionary keys are case-sensitive; the correct key is 'Age'.

🚀 Application

expert

Choosing AI method for spreadsheet anomaly detection

You want to detect unusual transactions in a large spreadsheet using AI. Which method is best suited for this task?

A Manually review each transaction row for anomalies without AI assistance.

B Use clustering to group transactions but ignore outliers as they are not important.

C Apply unsupervised anomaly detection algorithms like Isolation Forest without labeled data.

D Use a supervised classification model trained on labeled normal and anomalous transactions.

💡 Hint

Consider if labeled anomaly data is available or not.

Unsupervised methods like Isolation Forest detect anomalies without needing labeled examples, ideal for unknown anomaly patterns.