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

  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Sep 28, 2026
  • Q & A: 146 Questions and Answers

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

Question #1

What is the function of the melt() operation in Pandas when reshaping wide-format data?

  • A. Converts column values into rows
  • B. Combines multiple DataFrames
  • C. Transposes the DataFrame
  • D. Sorts the DataFrame based on a column
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #2

Which characteristics are typically associated with supervised learning algorithms?
(Choose two)

  • A. They automatically detect outliers
  • B. They require labeled training data
  • C. They work only with numerical features
  • D. They are useful for regression and classification tasks
Reveal Solution  Discussion  0

Correct Answer: B,D  🗳️

Question #3

When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

  • A. plt.annotate()
  • B. plt.title()
  • C. plt.xlabel()
  • D. plt.legend()
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #4

Which function is used to reshape a NumPy array without modifying the original data values?

  • A. transpose()
  • B. resize()
  • C. flatten()
  • D. reshape()
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #5

When fetching DATETIME values from a SQL database into Python, which Python data type is most appropriate for storing and manipulating these values?

  • A. datetime.datetime
  • B. str
  • C. float
  • D. time.struct_time
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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