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Exam Code: PCAD-31-02
Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
Certification Provider: Python Institute
Corresponding Certification: Python Institute Certification

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PCAD-31-02

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Updated: Oct 12, 2026

No. of Questions: 146 Questions & Answers with Testing Engine

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Python Institute PCAD-31-02 Exam Overview:

Certification Vendor:Python Institute
Exam Name:Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
Exam Number:PCAD-31-02
Passing Score:70%
Exam Format:Code Completion, Fill in the Blank, Multiple Choice, Multiple Select
Related Certifications:PCEP - Certified Entry-Level Python Programmer
PCAP - Certified Associate in Python Programming
Available Languages:English
Certificate Validity Period:No expiration (lifetime validity)
Real Exam Qty:40
Exam Price:$295 USD
Exam Duration:65 minutes
Sample Questions:Python Institute PCAD-31-02 Sample Questions
Exam Way:Online proctored exam through Pearson VUE testing centers
Pre Condition:PCEP certification is recommended but not required. Basic Python programming knowledge is expected.
Official Syllabus URL:https://pythoninstitute.org/certifications/pcad

Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

Question #1

Which characteristics are commonly associated with cloud-based data storage solutions?

  • A. High availability and redundancy
  • B. Elastic scalability
  • C. Physical proximity to user machines
  • D. Requires dedicated local hardware
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #2

Which practices help ensure secure and effective execution of SQL queries in Python scripts?
(Choose all that apply)

  • A. Wrapping SQL commands in try-except blocks
  • B. Using string formatting to build SQL queries dynamically
  • C. Using string concatenation to insert variables into SQL
  • D. Employing parameterized statements for user-supplied data
Reveal Solution  Discussion  0

Correct Answer: A,D  🗳️

Question #3

When analyzing a sales dataset using Pandas, which of the following techniques help extract key insights by grouping and summarizing the data?
(Choose two)

  • A. Using rename() to change column headers
  • B. Using groupby() to compute total sales per region
  • C. Applying agg() to calculate multiple metrics like mean and max
  • D. Using fillna() to replace missing product prices
Reveal Solution  Discussion  0

Correct Answer: B,C  🗳️

Question #4

Which chart types are commonly used to represent relationships between two numerical variables in Python-based visualizations?
(choose two)
Response:

  • A. Scatter plot
  • B. Line chart
  • C. Heatmap
  • D. Histogram
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

Question #5

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

  • A. Connection object to interface with the database
  • B. Cursor object for executing queries
  • C. Database driver or module (e.g., sqlite3)
  • D. Python's random module for generating keys
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

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