Python Institute PCAD-31-02 Q&A - in .pdf

  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Aug 13, 2026
  • Q & A: 146 Questions and Answers
  • PDF Price: $59.98
  • Printable Python Institute PCAD-31-02 PDF Format. It is an electronic file format regardless of the operating system platform.
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Python Institute PCAD-31-02 Q&A - Testing Engine

  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Aug 13, 2026
  • Q & A: 146 Questions and Answers
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Python Institute PCAD-31-02 Exam Syllabus Topics:
SectionWeightObjectives
Topic 1: Applied Data Analysis Projects20%- Exploratory Data Analysis (EDA)
  • 1. Data distribution analysis
  • 2. Correlation analysis
  • 3. Descriptive statistics computation
  • 4. Pattern identification
- Data Analysis Workflow
  • 1. Results interpretation and presentation
  • 2. Data exploration and cleaning
  • 3. Analysis and modeling
  • 4. Problem definition
Topic 2: Data Analysis Fundamentals20%- Data Collection and Preparation
  • 1. Data cleaning and preprocessing basics
  • 2. Data import/export operations
  • 3. Data sources and acquisition methods
- Introduction to Data Analysis
  • 1. Data analysis process lifecycle
  • 2. Types of data (structured, unstructured, semi-structured)
  • 3. Data analysis concepts and terminology
Topic 3: Python Programming for Data Analysis30%- Control Flow and Functions
  • 1. Loops (for, while)
  • 2. Function definitions and parameters
  • 3. Return values and scope
  • 4. Conditional statements (if, elif, else)
- Python Data Types and Structures
  • 1. Numbers, strings, booleans
  • 2. Lists, tuples, dictionaries, sets
  • 3. Data type conversions
- File Operations
  • 1. Context managers (with statement)
  • 2. Reading from files (text, CSV)
  • 3. Writing to files
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
- NumPy Fundamentals
  • 1. NumPy arrays and operations
  • 2. Vectorized operations
  • 3. Array indexing and slicing
  • 4. Basic statistical functions
- Data Visualization
  • 1. Matplotlib basics
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Seaborn introduction
  • 4. Customizing plots
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:

1. Which SQL commands can be used to modify the contents of a table's data directly?
(Choose all that apply)

A) SELECT
B) DELETE
C) INSERT
D) UPDATE


2. Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

A) Polynomial regression
B) Linear regression
C) Decision tree regression
D) Logistic regression


3. What is the purpose of using the groupby() function in a Pandas DataFrame when analyzing a dataset with multiple categories and numerical values?

A) To filter rows based on index values
B) To sort the DataFrame by a specific column
C) To append new rows to the DataFrame
D) To split the data into subgroups for aggregation


4. What is the primary purpose of using annotations in a data visualization?

A) To ensure faster rendering in large datasets
B) To highlight important data points and provide contextual information
C) To replace the need for axes
D) To enhance interactivity in static plots


5. What is the outcome of the following code?
data = [5, 10, 15]
result = [x**2 for x in data if x > 5]
print(result)

A) [25, 100, 225]
B) [100, 225]
C) [10, 15]
D) [25, 100]


Solutions:

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

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