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

PCAD-31-02 actual test
  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Aug 11, 2026
  • Q & A: 146 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $59.99  

About Python Institute PCAD-31-02 Exam

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

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

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


2. What is the correct SQL statement to permanently remove a table named customers from a database?

A) REMOVE TABLE customers;
B) TRUNCATE TABLE customers;
C) DROP TABLE customers;
D) DELETE TABLE customers;


3. Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

A) To optimize the model's training accuracy
B) To evaluate the model's performance on unseen data
C) To ensure the loss function is minimized
D) To reduce the number of iterations during training


4. Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

A) One-hot encoding
B) Z-score normalization
C) Min-max normalization
D) Log transformation


5. Which method is typically used in Pandas to check if a column contains only values within an expected range?

A) df.clip()
B) df.isin()
C) df.sort_values()
D) df.sample()


Solutions:

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

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