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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Python Programming for Data Analysis	30%	- Control Flow and Functions 1. Conditional statements (if, elif, else) 2. Return values and scope 3. Function definitions and parameters 4. Loops (for, while) - Python Data Types and Structures 1. Lists, tuples, dictionaries, sets 2. Data type conversions 3. Numbers, strings, booleans - File Operations 1. Writing to files 2. Reading from files (text, CSV) 3. Context managers (with statement)
Applied Data Analysis Projects	20%	- Exploratory Data Analysis (EDA) 1. Pattern identification 2. Correlation analysis 3. Data distribution analysis 4. Descriptive statistics computation - Data Analysis Workflow 1. Results interpretation and presentation 2. Data exploration and cleaning 3. Problem definition 4. Analysis and modeling
Working with Data Using Python Libraries	30%	- Data Visualization 1. Creating basic charts (line, bar, scatter, histogram) 2. Customizing plots 3. Matplotlib basics 4. Seaborn introduction - Pandas Library 1. Series and DataFrame structures 2. Data selection and filtering 3. DataFrame operations (merge, join, concat) 4. Handling missing data 5. GroupBy operations - NumPy Fundamentals 1. Vectorized operations 2. Basic statistical functions 3. NumPy arrays and operations 4. Array indexing and slicing
Data Analysis Fundamentals	20%	- Data Collection and Preparation 1. Data cleaning and preprocessing basics 2. Data sources and acquisition methods 3. Data import/export operations - Introduction to Data Analysis 1. Data analysis process lifecycle 2. Data analysis concepts and terminology 3. Types of data (structured, unstructured, semi-structured)

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q24-Q29):

NEW QUESTION # 24
Why is it important to manage object identity correctly in complex data analysis pipelines?

A. It avoids redundant copies and preserves shared state when required
B. It guarantees consistent hashing of string values
C. It prevents reloading modules unnecessarily
D. It ensures type casting is consistently applied

Answer: A

NEW QUESTION # 25
What is the purpose of using the groupby() function in a Pandas DataFrame when analyzing a dataset with multiple categories and numerical values?

A. To filter rows based on index values
B. To append new rows to the DataFrame
C. To split the data into subgroups for aggregation
D. To sort the DataFrame by a specific column

Answer: C

NEW QUESTION # 26
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

A. (4, 5, 6)
B. Error
C. 0
D. 5.0

Answer: D

NEW QUESTION # 27
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

A. list comprehensions
B. decorators
C. while loops
D. match-case statements

Answer: A,C

NEW QUESTION # 28
What is the outcome of the following code?
data = [5, 10, 15]
result = [x**2 for x in data if x > 5]
print(result)

A. [25, 100]
B. [10, 15]
C. [25, 100, 225]
D. [100, 225]

Answer: D

NEW QUESTION # 29
......

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