Question 1

You have a DataFrame named df that has three columns: 'A', 'B', 'C'. The DataFrame has 100 rows. Your task is to create a DataFrame where each column 'A', 'B', 'C' is transformed into 3 columns each: 'A_min', 'A_max', 'A_mean', 'B_min', 'B_max', 'B_mean', 'C_min', 'C_max', 'C_mean'. Which of the following code snippets accomplishes this task?

Options :

A : new_df = df.groupby(['A', 'B', 'C']).agg(['min', 'max', 'mean'])

B : new_df = df.apply(['min', 'max', 'mean'])

C : new_df = df.pivot(columns=['A', 'B', 'C'], values=['min', 'max', 'mean'])

D : new_df = df.aggregate(

{

'A': ['min', 'max', 'mean'],

'B': ['min', 'max', 'mean'],

'C': ['min', 'max', 'mean'],

}

)

Answer: D

Question 2

In a regression analysis of employee job satisfaction against years of experience, the p-value for the 'years of experience' variable is found to be 0.12. How should you interpret this result at a 0.05 significance level?

Options :

A : A p-value of 0.12 confirms that years of experience and job satisfaction are positively correlated.

B : Years of experience is not a statistically significant predictor of job satisfaction.

C : Years of experience is a statistically significant predictor of job satisfaction.

D : A p-value of 0.12 means that years of experience has a 12% impact on job satisfaction.

Answer: B

Question 3

You are pulling data from various databases using Python to compile a large dataset for analysis. How would you ensure the data’s accuracy and reliability?

Options :

A : Use the most recent data available in all databases.

B : Write the Python script in a way that it runs faster to save computational resources.

C : Compare a random subset of data with source records to validate accuracy.

D : Perform exploratory data analysis (EDA) only after data cleaning is done.

Answer: C

Question 4

You have a dataset of a retail company's sales transactions, which includes details like customer ID, transaction amount, transaction date, and store ID. You're tasked with summarizing monthly revenue for each store. What would be the most appropriate data aggregation technique?

Options :

A : Group the data by store ID and sum the transaction amounts daily, then take the average to get the monthly revenue.

B : Group the data by customer ID and store ID, then sum the transaction amounts and divide by the number of unique customers for each month.

C : Group the data by transaction date and calculate the daily revenue, then aggregate this to the monthly level without considering store ID.

D : Group the data by store ID and sum the transaction amounts for each month.

Answer: D

Question 5

You are working with a large dataset containing multi-dimensional features related to customer behavior. The dataset is too large and complex to analyze easily. What technique could be most effective for reducing the dataset's dimensionality while retaining most of its original variance?

Options :

A : Principal Component Analysis (PCA)

B : One-hot encoding

C : Linear Discriminant Analysis (LDA)

D : Max-Min Scaling

Answer: A

Smartly Prepare Exam with Free Online PCED-30-01 Practice Test

Buying Options: