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

  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Aug 04, 2026     Q & A: 146 Questions and Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)

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


2. What is the outcome of the following code?
data = [5, 10, 15]
result = [x**2 for x in data if x > 5]
print(result)

A) [25, 100]
B) [100, 225]
C) [25, 100, 225]
D) [10, 15]


3. Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

A) Ensuring consistent data types
B) Dropping column headers
C) Converting the DataFrame to a Series
D) Resetting the index after filtering


4. Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

A) Z-score normalization
B) Log transformation
C) Min-max normalization
D) One-hot encoding


5. 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 helps increase sample size
D) It ensures training data is intentionally overfitted


Solutions:

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

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