Python Institute : PCAD-31-02 Questions & Answers

Last Updated: Jul 22, 2026

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

A) Database driver or module (e.g., sqlite3)
B) Cursor object for executing queries
C) Connection object to interface with the database
D) Python's random module for generating keys


2. Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

A) Linear regression
B) Decision tree regression
C) Polynomial regression
D) Logistic regression


3. Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

A) Replacing inconsistent labels with a standardized value
B) Converting all values to integers
C) Removing rows with invalid labels
D) Normalizing using min-max scaling


4. What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

A) Connect → Commit → Execute → Close
B) Connect → Execute → Commit → Close
C) Execute → Connect → Commit → Close
D) Commit → Connect → Execute → Close


5. Which file format is most suitable for exchanging large tabular datasets with consistent column data types across systems?

A) .txt
B) .csv
C) .json
D) .xml


Solutions:

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

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