PCAD-31-02 Examcollection Vce, Exam PCAD-31-02 Study Solutions

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q91-Q96):

NEW QUESTION # 91
What is the main purpose of using the bootstrapping technique in inferential statistics?

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

Answer: C

NEW QUESTION # 92
Which type of chart is most appropriate when comparing the distribution of a numerical variable across multiple categories?

A. Pie chart
B. Box plot
C. Scatter plot
D. Line chart

Answer: B

NEW QUESTION # 93
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

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

Answer: A,B

NEW QUESTION # 94
Which method is used after grouping a DataFrame to compute the average value for each group in a specific column?

A. groupby().aggregate()
B. groupby().max()
C. groupby().count()
D. groupby().mean()

Answer: D

NEW QUESTION # 95
What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?

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

Answer: D

NEW QUESTION # 96
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