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Python Institute PCAD-31-02 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis Fundamentals | 20% | - Introduction to Data Analysis
|
| Topic 2: Working with Data Using Python Libraries | 30% | - Pandas Library
|
| Topic 3: Applied Data Analysis Projects | 20% | - Data Analysis Workflow
|
| Topic 4: Python Programming for Data Analysis | 30% | - Control Flow and Functions
|
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
1. Which actions are considered part of basic data validation tasks in Python using Pandas?
(Choose two)
A) Sorting the data frame by index
B) Checking for duplicate rows
C) Confirming numeric columns contain only integers
D) Replacing NULL values with placeholders
2. What may occur if a model is evaluated using the same data it was trained on?
A) Overfitting
B) Overgeneralization
C) Data leakage
D) Underfitting
3. Why is it important to adjust data presentations based on the audience's background?
A) To ensure the data is understood and supports actionable insights
B) To simplify all metrics to percentages only
C) To avoid using charts altogether
D) To include as many technical terms as possible
4. Which of the following statements correctly describe best practices when managing Python modules and packages in data analytics workflows?
(Choose two)
A) Always install packages system-wide for accessibility
B) Regularly update all packages using pip update all
C) Use PIP to install and uninstall third-party libraries as needed
D) Use virtual environments to isolate project dependencies
5. What is the best approach when comparing SQL NULL values with Python data during conditional checks?
A) Convert all NULL to string before comparison
B) Use Python's None and SQL's IS NULL clause
C) Replace NULL with zero before importing
D) Use == None directly in SQL queries
Solutions:
| Question # 1 Answer: B,C | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: C,D | Question # 5 Answer: B |
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