Free Practice Python Institute PCED-30-01 Exam Questions 2025

Stay ahead with 100% Free Certified Entry-Level Data Analyst with Python PCED-30-01 Dumps Practice Questions

Page: 1 / 77

Total 382 Questions | Updated On: Apr 11, 2025

Add To Cart

Question 1

You have a DataFrame df that contains duplicate rows. Your task is to remove duplicates based on two columns: 'A' and 'B'. What is the correct line of code to remove such duplicates while keeping the first occurrence?

A : df.drop_duplicates(subset=['A', 'B'], keep=False)

B : df.drop_duplicates()

C : df.unique(subset=['A', 'B'])

D : df.drop_duplicates(subset=['A', 'B'], keep='first')

Answer: D

Question 2

Which of the following cross-validation techniques is most appropriate when you are working with a dataset where the target variable's classes are imbalanced?

A : Leave-One-Out Cross-Validation (LOOCV)

B : k-Fold Cross-Validation

C : Stratified k-Fold Cross-Validation

D : Random Split Cross-Validation

Answer: C

Question 3

You are given a DataFrame df with a column 'Date' of type string in the format 'YYYY-MM-DD'. You want to filter the rows where the year is 2020. Which of the following code snippets is the most efficient way to do so?

A : df[pd.to_datetime(df['Date']).dt.year == 2020]

B : df[df['Date'].str.contains('2020')]

C : df[df['Date'].str.slice(0, 4) == '2020']

D : df[df['Date'].apply(lambda x: x.split('-')[0]) == '2020']

Answer: C

Question 4

You are analyzing the relationship between hours of study and exam scores among students. After calculating, you find a correlation coefficient (r) of 0.9. Which of the following is the best interpretation?

A : Hours of study has a strong, positive correlation with exam scores

B : Hours of study has a moderate, negative effect on exam scores

C : Exam scores have no relationship with hours of study

D : Hours of study causes high exam scores

Answer: A

Question 5

You are working with a dataset that has a mix of numerical and textual data. Your job is to synthesize this information into a more digestible form. What technique could you use to handle both types of data effectively for downstream analysis?

A : Run a Principal Component Analysis (PCA) on the entire dataset.

B : Implement a Decision Tree algorithm to identify the most important features.

C : Use k-Nearest Neighbors (k-NN) to classify data points.

D : Utilize Natural Language Processing (NLP) for textual data and Min-Max scaling for numerical data.

Answer: D

Page: 1 / 77

Total 382 Questions | Updated On: Apr 11, 2025

Add To Cart