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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q85-Q90):

NEW QUESTION # 85
In a scatter plot showing the relationship between advertising spend and sales revenue, the points form a tight upward-sloping pattern. What conclusion can be drawn?

A. The correlation is weak and likely negative
B. The data shows no apparent relationship
C. There is a strong positive correlation
D. Advertising has no effect on sales

Answer: C

NEW QUESTION # 86
Which of the following techniques are commonly used in the standardization of textual or categorical data during preprocessing?
(Choose two)

A. Trimming whitespace from string values
B. Applying logarithmic transformation to skewed data
C. Scaling numeric features to a fixed range
D. Replacing misspellings with a canonical category label

Answer: A,D

NEW QUESTION # 87
Why is it important to define and apply validation rules consistently across all stages of a data pipeline?

A. It guarantees compatibility with visualization tools
B. It automatically optimizes SQL queries
C. It reduces processing time by skipping null checks
D. It prevents the propagation of corrupted or invalid data

Answer: A

NEW QUESTION # 88
What is the main reason to remove duplicate records before performing analysis?

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

Answer: C

NEW QUESTION # 89
What is the purpose of using df.loc[2:5, 'column1'] in a Pandas DataFrame?

A. Returns the entire DataFrame
B. Returns rows 2 to 4 in 'column1'
C. Returns a new DataFrame excluding 'column1'
D. Returns rows 2 through 5 in 'column1'

Answer: D

NEW QUESTION # 90
......

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