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PCAD-31-02 pdf
  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Aug 22, 2026
  • Q & A: 146 Questions and Answers
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  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Aug 22, 2026
  • Q & A: 146 Questions and Answers
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  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Aug 22, 2026
  • Q & A: 146 Questions and Answers
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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Python Programming for Data Analysis30%- 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. Data type conversions
  • 3. Numbers, strings, booleans
- Control Flow and Functions
  • 1. Conditional statements (if, elif, else)
  • 2. Function definitions and parameters
  • 3. Loops (for, while)
  • 4. Return values and scope
Topic 2: Applied Data Analysis Projects20%- Data Analysis Workflow
  • 1. Problem definition
  • 2. Data exploration and cleaning
  • 3. Results interpretation and presentation
  • 4. Analysis and modeling
- Exploratory Data Analysis (EDA)
  • 1. Data distribution analysis
  • 2. Descriptive statistics computation
  • 3. Correlation analysis
  • 4. Pattern identification
Topic 3: Data Analysis Fundamentals20%- Data Collection and Preparation
  • 1. Data cleaning and preprocessing basics
  • 2. Data import/export operations
  • 3. Data sources and acquisition methods
- Introduction to Data Analysis
  • 1. Data analysis process lifecycle
  • 2. Data analysis concepts and terminology
  • 3. Types of data (structured, unstructured, semi-structured)
Topic 4: Working with Data Using Python Libraries30%- Pandas Library
  • 1. Data selection and filtering
  • 2. Handling missing data
  • 3. GroupBy operations
  • 4. Series and DataFrame structures
  • 5. DataFrame operations (merge, join, concat)
- NumPy Fundamentals
  • 1. NumPy arrays and operations
  • 2. Basic statistical functions
  • 3. Vectorized operations
  • 4. Array indexing and slicing
- Data Visualization
  • 1. Seaborn introduction
  • 2. Matplotlib basics
  • 3. Creating basic charts (line, bar, scatter, histogram)
  • 4. Customizing plots

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

1. Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

A) It creates a 3D surface plot using pandas
B) It overlays multiple plots in the same figure without axes
C) It configures multiple plots in a grid layout within a single figure
D) It links Matplotlib with Seaborn's grid styling


2. What is a major challenge in aggregating data from multiple sources?
Response:

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


3. Which Pandas methods are commonly used to extract central tendency and dispersion insights from a DataFrame?
(Choose two)

A) pivot_table()
B) median()
C) var()
D) sort_values()


4. What is the primary purpose of using a requirements.txt file in Python-based data projects?

A) To automatically compress source code for deployment
B) To track script runtime dependencies during execution
C) To store the current working directory of Python modules
D) To list the packages and versions required for consistent environment setup


5. What distinguishes structured data from unstructured data in the context of data modeling and storage?

A) Unstructured data cannot be stored or analyzed
B) Structured data cannot be stored in databases
C) Unstructured data contains only numeric values
D) Structured data follows a consistent schema or format


Solutions:

Question # 1
Answer: C
Question # 2
Answer: C
Question # 3
Answer: B,C
Question # 4
Answer: D
Question # 5
Answer: D

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