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Updated: Aug 07, 2026

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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
Certificate Validity Period:No expiration (lifetime validity)
Exam Format:Multiple Choice, Multiple Select, Fill in the Blank, Code Completion
Available Languages:English
Exam Duration:65 minutes
Passing Score:70%
Related Certifications:PCEP - Certified Entry-Level Python Programmer
PCAP - Certified Associate in Python Programming
Real Exam Qty:40
Exam Price:$295 USD
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
Applied Data Analysis Projects20%- Exploratory Data Analysis (EDA)
  • 1. Correlation analysis
  • 2. Data distribution analysis
  • 3. Descriptive statistics computation
  • 4. Pattern identification
- Data Analysis Workflow
  • 1. Results interpretation and presentation
  • 2. Problem definition
  • 3. Analysis and modeling
  • 4. Data exploration and cleaning
Python Programming for Data Analysis30%- Python Data Types and Structures
  • 1. Data type conversions
  • 2. Lists, tuples, dictionaries, sets
  • 3. Numbers, strings, booleans
- File Operations
  • 1. Writing to files
  • 2. Reading from files (text, CSV)
  • 3. Context managers (with statement)
- Control Flow and Functions
  • 1. Conditional statements (if, elif, else)
  • 2. Loops (for, while)
  • 3. Return values and scope
  • 4. Function definitions and parameters
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 import/export operations
  • 2. Data cleaning and preprocessing basics
  • 3. Data sources and acquisition methods
Working with Data Using Python Libraries30%- Pandas Library
  • 1. Data selection and filtering
  • 2. Handling missing data
  • 3. Series and DataFrame structures
  • 4. DataFrame operations (merge, join, concat)
  • 5. GroupBy operations
- Data Visualization
  • 1. Customizing plots
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Matplotlib basics
  • 4. Seaborn introduction
- NumPy Fundamentals
  • 1. Array indexing and slicing
  • 2. NumPy arrays and operations
  • 3. Basic statistical functions
  • 4. Vectorized operations

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

1. When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

A) plt.title()
B) plt.xlabel()
C) plt.legend()
D) plt.annotate()


2. Which characteristics are typically associated with unstructured data formats?
(Choose two)

A) Stored using fixed-width fields and records
B) Requires parsing or transformation before analysis
C) Highly tabular and query-friendly
D) Lacks predefined data schema


3. Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A) df.rename()
B) df.replace()
C) df.sort_values()
D) df.dropna()


4. What may occur if a model is evaluated using the same data it was trained on?

A) Underfitting
B) Overfitting
C) Data leakage
D) Overgeneralization


5. What does the standard deviation of a dataset represent in the context of statistical analysis?

A) The most frequently occurring value
B) The average squared distance from the mean
C) The number of outliers in the dataset
D) The sum of all values in the dataset


Solutions:

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

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