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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q135-Q140):

NEW QUESTION # 135
Which of the following techniques are commonly used in the standardization of textual or categorical data during preprocessing?
(Choose two)

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

Answer: A,B

NEW QUESTION # 136
What is the primary reason for using parameterized queries instead of directly formatting SQL strings in Python?

A. To allow dynamic table creation
B. To reduce the number of SQL statements
C. To prevent SQL injection vulnerabilities
D. To simplify data encryption

Answer: C

NEW QUESTION # 137
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

A. df['id'].duplicated().any()
B. df['id'].nunique()
C. df['id'].isnull().sum()
D. df['id'].fillna()

Answer: A

NEW QUESTION # 138
You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?

A. dictionary
B. set
C. tuple
D. list

Answer: B

NEW QUESTION # 139
When analyzing a sales dataset using Pandas, which of the following techniques help extract key insights by grouping and summarizing the data?
(Choose two)

A. Applying agg() to calculate multiple metrics like mean and max
B. Using groupby() to compute total sales per region
C. Using rename() to change column headers
D. Using fillna() to replace missing product prices

Answer: A,B

NEW QUESTION # 140
......

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