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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which practices are typically part of the data integration process?
(Choose two)

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

Answer: C,D

NEW QUESTION # 36
What is the purpose of using df.loc[2:5, 'column1'] in a Pandas DataFrame?

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

Answer: B

NEW QUESTION # 37
Which practices help ensure secure and effective execution of SQL queries in Python scripts?
(Choose all that apply)

A. Employing parameterized statements for user-supplied data
B. Using string formatting to build SQL queries dynamically
C. Wrapping SQL commands in try-except blocks
D. Using string concatenation to insert variables into SQL

Answer: A,C

NEW QUESTION # 38
Which characteristics are typically associated with unstructured data formats?
(Choose two)

A. Lacks predefined data schema
B. Highly tabular and query-friendly
C. Stored using fixed-width fields and records
D. Requires parsing or transformation before analysis

Answer: A,D

NEW QUESTION # 39
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A. df.replace()
B. df.dropna()
C. df.sort_values()
D. df.rename()

Answer: A

NEW QUESTION # 40
......

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