Python Institute PCED-30-01 Practice Exams
Last updated on Oct 21,2026- Exam Code: PCED-30-01
- Exam Name: Certified Entry-Level Data Analyst with Python
- Certification Provider: Python Institute
- Latest update: Oct 21,2026
What is the purpose of a box plot in statistical analysis?
- A . To show the distribution of data
- B . To calculate the mean of the data
- C . To conduct hypothesis tests
- D . To display correlation between variables
In Python, what does the ‘== operator’ do?
- A . Checks if two variables have the same memory address
- B . Checks if two variables have the same value
- C . Assigns a value to a variable
- D . Compares the memory addresses of two variables
Which statistical analysis technique is used to determine the relationship between two continuous variables?
- A . t-test
- B . ANOVA
- C . Pearson correlation
- D . Chi-square test
What is the process of transforming categorical variables into a numerical format for analysis called?
- A . Normalization
- B . Encoding
- C . Clustering
- D . Dimensionality Reduction
Which of the following is NOT an example of data cleaning in the pre-processing stage?
- A . Removing duplicate entries
- B . Converting categorical data to numerical values
- C . Handling missing values
- D . Aggregating data from multiple sources without validation
What type of plot is commonly used to visualize the relationship between two continuous variables?
- A . Histogram
- B . Scatter plot
- C . Bar chart
- D . Pie chart
Which of the following Python libraries is commonly used for data manipulation and analysis?
- A . Pandas
- B . Matplotlib
- C . NumPy
- D . Scikit-learn
How do you comment out a single line of code in Python?
- A . // This is a comment
- B . /* This is a comment */
- C . # This is a comment
What type of plot is commonly used to visualize the relationship between two numerical variables?
- A . Box plot
- B . Scatter plot
- C . Histogram
- D . Pie chart
In data pre-processing, what is the term used for handling missing values in a dataset?
- A . Data normalization
- B . Data standardization
- C . Imputation
- D . Feature extraction