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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Working with Data Using Python Libraries	30%	- Data Visualization 1. Creating basic charts (line, bar, scatter, histogram) 2. Matplotlib basics 3. Seaborn introduction 4. Customizing plots - NumPy Fundamentals 1. Basic statistical functions 2. NumPy arrays and operations 3. Vectorized operations 4. Array indexing and slicing - Pandas Library 1. Series and DataFrame structures 2. Handling missing data 3. Data selection and filtering 4. GroupBy operations 5. DataFrame operations (merge, join, concat)
Topic 2: Python Programming for Data Analysis	30%	- Python Data Types and Structures 1. Lists, tuples, dictionaries, sets 2. Numbers, strings, booleans 3. Data type conversions - File Operations 1. Context managers (with statement) 2. Reading from files (text, CSV) 3. Writing to files - Control Flow and Functions 1. Loops (for, while) 2. Conditional statements (if, elif, else) 3. Function definitions and parameters 4. Return values and scope
Topic 3: Applied Data Analysis Projects	20%	- Exploratory Data Analysis (EDA) 1. Correlation analysis 2. Descriptive statistics computation 3. Pattern identification 4. Data distribution analysis - Data Analysis Workflow 1. Data exploration and cleaning 2. Analysis and modeling 3. Results interpretation and presentation 4. Problem definition
Topic 4: Data Analysis Fundamentals	20%	- Data Collection and Preparation 1. Data import/export operations 2. Data sources and acquisition methods 3. Data cleaning and preprocessing basics - 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 (Q123-Q128):

NEW QUESTION # 123
Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?

A. Tuple unpacking
B. Custom functions
C. Lambda functions
D. Global variables

Answer: B

NEW QUESTION # 124
Which Python construct allows you to iterate over a list of dictionary values and apply conditional logic to accumulate numerical values that meet a certain threshold?

A. for loop with if condition
B. lambda expression with reduce
C. filter() function with map
D. while loop with try block

Answer: A

NEW QUESTION # 125
What is the main reason to remove duplicate records before performing analysis?

A. It ensures training data is intentionally overfitted
B. It helps increase sample size
C. It avoids converting data to JSON format
D. It reduces redundancy that can skew statistical metrics

Answer: D

NEW QUESTION # 126
How would you extract the last three rows of a DataFrame df using position-based indexing?

A. df.iloc[-3:]
B. df.loc[-3:]
C. df.tail(3)
D. df[-3]

Answer: A

NEW QUESTION # 127
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 dropna() with axis=1
D. Use drop() to remove null rows

Answer: B

NEW QUESTION # 128
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