Latest PCAD-31-02 Exam Questions, PCAD-31-02 Exam Flashcards

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Python Programming for Data Analysis	30%	- Python Data Types and Structures 1. Numbers, strings, booleans 2. Lists, tuples, dictionaries, sets 3. Data type conversions - Control Flow and Functions 1. Return values and scope 2. Loops (for, while) 3. Function definitions and parameters 4. Conditional statements (if, elif, else) - File Operations 1. Reading from files (text, CSV) 2. Context managers (with statement) 3. Writing to files
Topic 2: Working with Data Using Python Libraries	30%	- Pandas Library 1. GroupBy operations 2. DataFrame operations (merge, join, concat) 3. Handling missing data 4. Series and DataFrame structures 5. Data selection and filtering - NumPy Fundamentals 1. Basic statistical functions 2. Vectorized operations 3. Array indexing and slicing 4. NumPy arrays and operations - Data Visualization 1. Matplotlib basics 2. Creating basic charts (line, bar, scatter, histogram) 3. Seaborn introduction 4. Customizing plots
Topic 3: Applied Data Analysis Projects	20%	- Data Analysis Workflow 1. Results interpretation and presentation 2. Analysis and modeling 3. Data exploration and cleaning 4. Problem definition - Exploratory Data Analysis (EDA) 1. Correlation analysis 2. Pattern identification 3. Data distribution analysis 4. Descriptive statistics computation
Topic 4: Data Analysis Fundamentals	20%	- Introduction to Data Analysis 1. Data analysis process lifecycle 2. Data analysis concepts and terminology 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 (Q112-Q117):

NEW QUESTION # 112
Which function is used to reshape a NumPy array without modifying the original data values?

A. reshape()
B. transpose()
C. resize()
D. flatten()

Answer: A

NEW QUESTION # 113
What is the main reason to remove duplicate records before performing analysis?

A. It avoids converting data to JSON format
B. It reduces redundancy that can skew statistical metrics
C. It ensures training data is intentionally overfitted
D. It helps increase sample size

Answer: B

NEW QUESTION # 114
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)

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

Answer: B,D

NEW QUESTION # 115
Which metric is most appropriate for evaluating the accuracy of a classification model?

A. Precision
B. Confusion matrix
C. Mean squared error
D. R-squared

Answer: A

NEW QUESTION # 116
What is the main purpose of using the bootstrapping technique in inferential statistics?

A. To filter outliers from a dataset
B. To estimate the sampling distribution by resampling with replacement
C. To validate the syntax of SQL queries in Python
D. To increase the number of features in a model

Answer: B

NEW QUESTION # 117
......

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