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Python Institute PCAD-31-02 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Python Programming for Data Analysis | 30% | - File Operations
|
| Topic 2: Working with Data Using Python Libraries | 30% | - Data Visualization
|
| Topic 3: Applied Data Analysis Projects | 20% | - Exploratory Data Analysis (EDA)
|
| Topic 4: Data Analysis Fundamentals | 20% | - Introduction to Data Analysis
|
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
What is the most efficient way to handle missing values in a Pandas DataFrame when preparing a dataset for analysis that must maintain original index structure and avoid row deletions?
- A. Apply interpolate() method
- B. Use fillna() with a constant value
- C. Use drop() to remove null rows
- D. Use dropna() with axis=1
Correct Answer: B 🗳️
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?
- A. df.sort_values()
- B. df.replace()
- C. df.dropna()
- D. df.rename()
Correct Answer: B 🗳️
Which of the following would most likely be classified as unstructured data?
- A. Product ID and price list in a CSV
- B. Collection of customer service audio recordings
- C. Inventory levels in a spreadsheet
- D. Normalized employee database in PostgreSQL
Correct Answer: B 🗳️
What is the main reason to remove duplicate records before performing analysis?
- A. It reduces redundancy that can skew statistical metrics
- B. It avoids converting data to JSON format
- C. It helps increase sample size
- D. It ensures training data is intentionally overfitted
Correct Answer: A 🗳️
Which techniques are commonly used to manage type conversion between SQL and Python when importing database values?
(Choose two)
- A. Defining column types in Python using sqlite3.register_converter()
- B. Using a mapping function to convert SQL types into Python objects
- C. Ignoring data types and treating everything as strings
- D. Automatically casting Python variables to SQL column types without validation
Correct Answer: A,B 🗳️
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