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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)

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

Answer: B,C

NEW QUESTION # 43
Why is min-max scaling applied before using algorithms like K-Nearest Neighbors or SVM?

A. To compress all values into integers
B. To convert strings into numeric values
C. To make all features interpretable as binary values
D. To prevent features with larger scales from dominating distance calculations

Answer: D

NEW QUESTION # 44
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 automatically detect outliers
D. They require labeled training data

Answer: A,D

NEW QUESTION # 45
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)

A. Box plots
B. Correlation matrix
C. Histogram analysis
D. Scatter plots

Answer: B,D

NEW QUESTION # 46
Which of the following correctly demonstrates the use of parameterized queries using Python's sqlite3 module?

A. cursor.execute("SELECT * FROM users WHERE id = " + user_id)
B. cursor.execute("SELECT * FROM users WHERE id = ?", user_id)
C. cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
D. cursor.execute("SELECT * FROM users WHERE id = ':id'", {'id': user_id})

Answer: C

NEW QUESTION # 47
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