Python Institute PCAD-31-02 real exam prep : 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: Aug 01, 2026
  • Q&As: 146 Questions and Answers

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PCAD-31-02 exam dumps

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. What is the main reason to remove duplicate records before performing analysis?

A) It helps increase sample size
B) It reduces redundancy that can skew statistical metrics
C) It ensures training data is intentionally overfitted
D) It avoids converting data to JSON format


2. Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

A) GROUP BY
B) ORDER BY
C) HAVING
D) WHERE


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

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


4. Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

A) Converting all values to integers
B) Normalizing using min-max scaling
C) Removing rows with invalid labels
D) Replacing inconsistent labels with a standardized value


5. What result will the following Pandas expression return: df['Age'].notnull().all()?

A) Returns the number of NULL values in 'Age'
B) Returns True if all values in 'Age' are not NULL
C) Returns True if all values in 'Age' are NULL
D) Returns True if at least one value in 'Age' is not NULL


Solutions:

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

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