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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q74-Q79):

NEW QUESTION # 74
Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)

A. Logging error messages for later inspection during failures
B. Suppressing all error output using pass
C. Catching all exceptions using a generic except: clause without specifying the error
D. Using specific exception types like FileNotFoundError or ZeroDivisionError

Answer: A,B

NEW QUESTION # 75
When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

A. Cursor object for executing queries
B. Database driver or module (e.g., sqlite3)
C. Python's random module for generating keys
D. Connection object to interface with the database

Answer: B,D

NEW QUESTION # 76
Which characteristics are commonly associated with cloud-based data storage solutions?

A. Elastic scalability
B. Requires dedicated local hardware
C. Physical proximity to user machines
D. High availability and redundancy

Answer: A

NEW QUESTION # 77
What is the main purpose of validating a dataset before applying statistical analysis?

A. To ensure the dataset meets expected quality and structure
B. To visualize patterns more clearly
C. To sort the data alphabetically
D. To generate more columns from the dataset

Answer: A

NEW QUESTION # 78
What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?

A. A moderate non-linear association
B. A weak positive relationship
C. No correlation
D. A strong negative linear relationship

Answer: D

NEW QUESTION # 79
......

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