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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q125-Q130):

NEW QUESTION # 125
What is the correct SQL statement to permanently remove a table named customers from a database?

A. TRUNCATE TABLE customers;
B. REMOVE TABLE customers;
C. DROP TABLE customers;
D. DELETE TABLE customers;

Answer: C

NEW QUESTION # 126
Which operations are valid and commonly used on Python lists during data transformation tasks?
(Choose two)

A. Slicing
B. Index-based iteration
C. Append
D. Key-value mapping

Answer: A,C

NEW QUESTION # 127
Which practices are essential to ensure a fair evaluation of a machine learning model's performance?
(Choose two)

A. Splitting data into training and test sets
B. Randomizing the test dataset labels
C. Using cross-validation to assess generalizability
D. Training on the entire dataset including test records

Answer: A,C

NEW QUESTION # 128
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.sort_values()
B. df.dropna()
C. df.replace()
D. df.rename()

Answer: C

NEW QUESTION # 129
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

A. Linear regression requires target variables to be binary
B. Linear regression applies only to classification tasks
C. Logistic regression outputs probabilities, while linear regression predicts continuous values
D. Logistic regression uses the logit function to model relationships

Answer: C,D

NEW QUESTION # 130
......

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