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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) : 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 16, 2026

Q&A Number: 146 Q&As

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
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
- File Operations
  • 1. Context managers (with statement)
  • 2. Writing to files
  • 3. Reading from files (text, CSV)
- Python Data Types and Structures
  • 1. Lists, tuples, dictionaries, sets
  • 2. Numbers, strings, booleans
  • 3. Data type conversions
Working with Data Using Python Libraries30%- Data Visualization
  • 1. Matplotlib basics
  • 2. Customizing plots
  • 3. Creating basic charts (line, bar, scatter, histogram)
  • 4. Seaborn introduction
- Pandas Library
  • 1. DataFrame operations (merge, join, concat)
  • 2. GroupBy operations
  • 3. Series and DataFrame structures
  • 4. Data selection and filtering
  • 5. Handling missing data
- NumPy Fundamentals
  • 1. Basic statistical functions
  • 2. Array indexing and slicing
  • 3. NumPy arrays and operations
  • 4. Vectorized operations
Data Analysis Fundamentals20%- Introduction to Data Analysis
  • 1. Types of data (structured, unstructured, semi-structured)
  • 2. Data analysis process lifecycle
  • 3. Data analysis concepts and terminology
- Data Collection and Preparation
  • 1. Data sources and acquisition methods
  • 2. Data cleaning and preprocessing basics
  • 3. Data import/export operations
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. Problem definition
  • 4. Analysis and modeling

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

1. What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

A) Parquet supports schema evolution and columnar storage
B) Parquet offers row-based compression
C) Parquet files can only be used with Excel
D) Parquet stores data in plain text, making it human-readable


2. Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

A) Use the requests module to retrieve JSON responses
B) Load CSV directly with re.compile()
C) Open Excel files using matplotlib
D) Access data using requests.get() and parse it with .json()


3. What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?

A) A weak positive relationship
B) No correlation
C) A moderate non-linear association
D) A strong negative linear relationship


4. In a scatter plot showing the relationship between advertising spend and sales revenue, the points form a tight upward-sloping pattern. What conclusion can be drawn?

A) The data shows no apparent relationship
B) Advertising has no effect on sales
C) The correlation is weak and likely negative
D) There is a strong positive correlation


5. What is the main purpose of using the bootstrapping technique in inferential statistics?

A) To estimate the sampling distribution by resampling with replacement
B) To increase the number of features in a model
C) To filter outliers from a dataset
D) To validate the syntax of SQL queries in Python


Solutions:

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

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