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Python Institute PCAD-31-02 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Applied Data Analysis Projects | 20% | - Exploratory Data Analysis (EDA)
|
| Working with Data Using Python Libraries | 30% | - NumPy Fundamentals
|
| Python Programming for Data Analysis | 30% | - Python Data Types and Structures
|
| Data Analysis Fundamentals | 20% | - Introduction to Data Analysis
|
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
Question 1
What is the main purpose of using the bootstrapping technique in inferential statistics?
A. To validate the syntax of SQL queries in Python
B. To increase the number of features in a model
C. To filter outliers from a dataset
D. To estimate the sampling distribution by resampling with replacement
Question 2
What is the function of the melt() operation in Pandas when reshaping wide-format data?
A. Sorts the DataFrame based on a column
B. Transposes the DataFrame
C. Converts column values into rows
D. Combines multiple DataFrames
Question 3
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?
A. DataFrame.drop_duplicates()
B. DataFrame.unique()
C. DataFrame.remove_duplicates()
D. DataFrame.nunique()
Question 4
Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?
A. To optimize the model's training accuracy
B. To evaluate the model's performance on unseen data
C. To ensure the loss function is minimized
D. To reduce the number of iterations during training
Question 5
Which practices are typically part of the data integration process?
(Choose two)
A. Format standardization
B. Neural network training
C. Data encryption
D. Schema alignment
Solutions:
| Question 1 Answer: D | Question 2 Answer: C | Question 3 Answer: C | Question 4 Answer: B | Question 5 Answer: A,D |
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