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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q100-Q105):

NEW QUESTION # 100
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. Advertising has no effect on sales
B. There is a strong positive correlation
C. The data shows no apparent relationship
D. The correlation is weak and likely negative

Answer: B

NEW QUESTION # 101
Which practices are typically part of the data integration process?
(Choose two)

A. Schema alignment
B. Neural network training
C. Data encryption
D. Format standardization

Answer: A,D

NEW QUESTION # 102
What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?

A. Missing foreign key constraints
B. Slower database performance
C. Increased memory usage
D. Exposure to SQL injection attacks

Answer: D

NEW QUESTION # 103
Which characteristics are typically associated with supervised learning algorithms?
(Choose two)

A. They are useful for regression and classification tasks
B. They work only with numerical features
C. They require labeled training data
D. They automatically detect outliers

Answer: A,C

NEW QUESTION # 104
Which of the following statements best describes the structural relationship between Pandas Series and DataFrames?

A. A Series can only be created from a DataFrame
B. A DataFrame is a one-dimensional array of Series
C. A Series contains multiple DataFrames
D. A DataFrame is a two-dimensional container of Series objects

Answer: D

NEW QUESTION # 105
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