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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q123-Q128):

NEW QUESTION # 123
Which data collection method is most appropriate when gathering opinions from a target audience at scale?

A. Web scraping
B. Transactional data
C. Sensor logs
D. Surveys

Answer: D

NEW QUESTION # 124
Which characteristics are typically associated with supervised learning algorithms?
(Choose two)

A. They require labeled training data
B. They work only with numerical features
C. They are useful for regression and classification tasks
D. They automatically detect outliers

Answer: A,C

NEW QUESTION # 125
Which of the following techniques are commonly used in the standardization of textual or categorical data during preprocessing?
(Choose two)

A. Applying logarithmic transformation to skewed data
B. Trimming whitespace from string values
C. Scaling numeric features to a fixed range
D. Replacing misspellings with a canonical category label

Answer: B,D

NEW QUESTION # 126
Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

A. To optimize the model's training accuracy
B. To evaluate the model's performance on unseen data
C. To ensure the loss function is minimized
D. To reduce the number of iterations during training

Answer: B

NEW QUESTION # 127
What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?

A. Missing foreign key constraints
B. Exposure to SQL injection attacks
C. Increased memory usage
D. Slower database performance

Answer: B

NEW QUESTION # 128
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