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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q102-Q107):

NEW QUESTION # 102
Which best practices help enhance database security when building Python data analysis pipelines?
(Choose two)

A. Limit database user permissions to only necessary operations
B. Construct queries using f-strings for readability
C. Store credentials securely using environment variables
D. Sanitize input by replacing dangerous characters with asterisks

Answer: A,C

NEW QUESTION # 103
What is the main purpose of validating a dataset before applying statistical analysis?

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

Answer: C

NEW QUESTION # 104
Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

A. Replacing inconsistent labels with a standardized value
B. Converting all values to integers
C. Normalizing using min-max scaling
D. Removing rows with invalid labels

Answer: A,D

NEW QUESTION # 105
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 # 106
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)

A. It requires a large population sample
B. It enables confidence interval estimation
C. It assumes a normal distribution
D. It uses random sampling with replacement

Answer: B,D

NEW QUESTION # 107
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