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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
1. Which practices help maintain data integrity across large datasets in Pandas workflows?
(Choose two)
A) Automatically converting all text to uppercase
B) Defining and enforcing schema constraints
C) Sorting columns alphabetically
D) Using assertions to enforce domain-specific rules
2. What is the function of the melt() operation in Pandas when reshaping wide-format data?
A) Combines multiple DataFrames
B) Converts column values into rows
C) Sorts the DataFrame based on a column
D) Transposes the DataFrame
3. Which of the following statements correctly describe best practices when managing Python modules and packages in data analytics workflows?
(Choose two)
A) Use virtual environments to isolate project dependencies
B) Regularly update all packages using pip update all
C) Always install packages system-wide for accessibility
D) Use PIP to install and uninstall third-party libraries as needed
4. What result will the following Pandas expression return: df['Age'].notnull().all()?
A) Returns True if at least one value in 'Age' is not NULL
B) Returns the number of NULL values in 'Age'
C) Returns True if all values in 'Age' are NULL
D) Returns True if all values in 'Age' are not NULL
5. What is the main reason for using try-except blocks in a data analysis script that reads multiple files?
A) To enforce strict type annotations
B) To automatically restart the script after execution
C) To ensure functions always return None
D) To gracefully handle runtime errors without crashing the program
Solutions:
| Question # 1 Answer: B,D | Question # 2 Answer: B | Question # 3 Answer: A,D | Question # 4 Answer: D | Question # 5 Answer: D |
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