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

  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Aug 12, 2026
  • Q & A: 146 Questions and Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A) df.rename()
B) df.dropna()
C) df.replace()
D) df.sort_values()


2. Which SQL commands are typically used to retrieve and manipulate data in a data analysis context?
(Choose all that apply)
Response:

A) ROLLBACK
B) INSERT
C) SELECT
D) UPDATE


3. What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?

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


4. Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)

A) Suppressing all error output using pass
B) Using specific exception types like FileNotFoundError or ZeroDivisionError
C) Catching all exceptions using a generic except: clause without specifying the error
D) Logging error messages for later inspection during failures


5. Which operations are valid when working with Pandas Series?
(Choose two)

A) Applying NumPy universal functions
B) Using .columns to rename
C) Merging by index using merge()
D) Arithmetic vector operations


Solutions:

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

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