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 13, 2026   Q&As: 146 Questions and Answers
  • Q & A: 146 Questions and Answers

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

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Python Institute PCAD-31-02 Exam Syllabus Topics:
SectionWeightObjectives
Data Analysis Fundamentals20%- Introduction to Data Analysis
  • 1. Data analysis process lifecycle
  • 2. Data analysis concepts and terminology
  • 3. Types of data (structured, unstructured, semi-structured)
- Data Collection and Preparation
  • 1. Data cleaning and preprocessing basics
  • 2. Data sources and acquisition methods
  • 3. Data import/export operations
Working with Data Using Python Libraries30%- NumPy Fundamentals
  • 1. Basic statistical functions
  • 2. NumPy arrays and operations
  • 3. Array indexing and slicing
  • 4. Vectorized operations
- Data Visualization
  • 1. Creating basic charts (line, bar, scatter, histogram)
  • 2. Seaborn introduction
  • 3. Customizing plots
  • 4. Matplotlib basics
- Pandas Library
  • 1. Series and DataFrame structures
  • 2. GroupBy operations
  • 3. Handling missing data
  • 4. DataFrame operations (merge, join, concat)
  • 5. Data selection and filtering
Python Programming for Data Analysis30%- Control Flow and Functions
  • 1. Loops (for, while)
  • 2. Return values and scope
  • 3. Conditional statements (if, elif, else)
  • 4. Function definitions and parameters
- File Operations
  • 1. Writing to files
  • 2. Reading from files (text, CSV)
  • 3. Context managers (with statement)
- Python Data Types and Structures
  • 1. Data type conversions
  • 2. Lists, tuples, dictionaries, sets
  • 3. Numbers, strings, booleans
Applied Data Analysis Projects20%- Exploratory Data Analysis (EDA)
  • 1. Pattern identification
  • 2. Correlation analysis
  • 3. Descriptive statistics computation
  • 4. Data distribution analysis
- Data Analysis Workflow
  • 1. Problem definition
  • 2. Results interpretation and presentation
  • 3. Analysis and modeling
  • 4. Data exploration and cleaning
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.dropna()
B) df.rename()
C) df.sort_values()
D) df.replace()


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

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


3. Which term best describes the process of combining customer data from multiple systems into a single unified dataset?

A) Data warehousing
B) Data integration
C) Data binning
D) Data normalization


4. Why might using a pie chart to represent category proportions be less effective than a bar chart?

A) Pie charts cannot be created using Seaborn
B) Bar charts are limited to one variable only
C) Pie charts display individual data points more clearly
D) Human perception is less accurate at judging angles than lengths


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

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


Solutions:

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

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