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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

A. Include concise textual summaries that highlight the chart's message
B. Avoid summarizing the data verbally
C. Use visualizations alone without titles or captions
D. Repeat all numerical data in both the chart and text

Answer: A

NEW QUESTION # 37
Which methods can be used to validate data types and structure in a Pandas DataFrame?
(Choose two)

A. df.dtypes
B. df.memory_usage()
C. df.to_csv()
D. df.info()

Answer: A,D

NEW QUESTION # 38
What is a major challenge in aggregating data from multiple sources?
Response:

A. Overfitting in predictive models
B. Excessive disk space usage
C. Increased algorithmic complexity
D. Duplicate records and format inconsistencies

Answer: D

NEW QUESTION # 39
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

A. Use the requests module to retrieve JSON responses
B. Access data using requests.get() and parse it with .json()
C. Open Excel files using matplotlib
D. Load CSV directly with re.compile()

Answer: A,B

NEW QUESTION # 40
What is the main reason to remove duplicate records before performing analysis?

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

Answer: D

NEW QUESTION # 41
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